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English to Serbian: heuristics and metaheuristics General field: Science Detailed field: Science (general)
Source text - English 1 HEURISTIC AND METAHEURISTIC TECHNIQUES B. Srdjevic©2009 OVERVIEW University of Novi Sad, Faculty of Agriculture University of Belgrade, Faculty of Agriculture Joint Diploma Master Study Programme: AGRICULTURAL WATER MANAGEMENT (LOLAqua) Subject: ADVANCED WATER RESOURCES SYSTEMS ANALYSIS Lecture SA21_2 TERMS Heuristics means "rules of thumb" or techniques that improve the average-case performance on a problemsolving task. In the context of optimization, a heuristics will discover good solutions for hard problems, often relatively quickly. Metaheuristics means "heuristics for managing heuristics". Metaheuristics control the application and interaction of one or more heuristics, searching for a better solution than any single heuristic would find on its own. The sharp distinction between heuristics and metaheuristics does not really exist. 2 COMBINATORIAL OPTIMIZATION (CO) Strong relation between H/MH and CO. CO is search for global optimum over large, very large or ‘infinitely large’ discrete solution domains. In fact, any optimization procedure seeks to find the optimal solution x*=opt{f(x)⎜xεS}, where f is the objective function, and S is its domain, i.e. the set of possible solutions to the given problem. A CO problem is an optimization problem where S has discrete members. Operator ‘opt’ stands for either ‘minimize’ or ‘maximize’. For simplicity hereafter it will refer to minimization. Consequently the objective function will be called a cost function. Switch to maximization may simply be done by reverting the sign of the function which would also revert its meaning appropriately to benefit instead of cost. COMBINATORIAL OPTIMIZATION (CO) When the problem complexity is low, either an exhaustive search of the space or deterministic algorithms may be used to obtain the solution. There are many deterministic algorithms grouped into different categories such as Linear Programming, Nonlinear Programming, Dynamic Programming, or Branch-and-Bound. For more difficult problems, heuristic, metaheuristic and stochastic search techniques appear to be the only techniques able to find the optimal or near the optimal point in a large solution space. They are usually gathered under common term Intelligent Search Methods and they are much more the subjects of Artificial Intelligence than the classical optimization theory . 3 H/MH ALGORITHMS Charactersitics generality ease of implementation robustness Major class of CO algorithms is called general iterative algorithms, also considered as a class of approximation algorithms. H/MH ALGORITHMS DESIGNING The design of h/m algorithms assumes that some of the following techniques and characteristics should be incorporated: • Hill-climbing techniques that can search for better solutions when given an initial solution as a starting point. • Iterative improvement heuristics, such as bottleneck reduction, that can be used to improve solutions through repeated application. • Knowledge-based search methods that can be used when one or more attributes of candidate solutions are important. 4 H/MH ALGORITHMS DESIGNING (cont) • Algorithms based on principles of biological evolution are known as evolutionary computation. These algorithms, such as genetic algorithms, are powerful problem-solving methods in which a population of candidate solutions "evolve", getting better and better according to the principle of "survival of the fittest". • Techniques based on principles of thermodynamics that mimic how liquids freeze and crystallize, or metals cool and anneal. Algorithms such as simulated annealing guide the search for a good solution by allowing worse solutions to be accepted subject to a "current temperature" value defined by the annealer's ''cooling schedule". • Procedures based on temporary elimination of backtracking, such as tabu search. H/MH ALGORITHMS CONSTRUCTING The most common way is: • Combine various elements of directed random search and iterative improvement. • Perform fine-tuning of the result to meet the target application's requirements. These requirements often include both performance and memory constraints. 5 H/MH ALGORITHMS MOST COMMONLY USED ALGORITHMS Genetic Algorithm (GA) Simulated Annealing (SA) Tabu Search (TS) H/MH ALGORITHMS Genetic Algorithm (GA) Simulated Annealing (SA) Tabu Search (TS) All are optimization heuristics with several similarities: • They are approximation (heuristic) algorithms, i.e. they do not guarantee finding an optimal solution. • They are blind, in that they do not know when an optimal solution is reached. They must be told when to stop. • All they have hill-climbing ability, i.e. they occasionally accept uphill (bad) moves. • They are all general and can easily be engineered to implement any combinatorial optimization problem; all that is required is to have a suitable solution representation, a cost function, and a mechanism to traverse the search space. • They asymptotically converge to an optimal solution, under certain conditions. • They are all robust in searching for best solution; genetic algorithm additionally exhibits parallel search strategy. 6 Non-Intelligent Search Methods Also known as WEAK (low level adaptive ) Search Methods BLIND SEARCH • The logic behind is referred to as generate and test. That means generate candidate solutions to some problem and then test whether a candidate solution is in fact the solution to the problem. • For example: solve 4x-3=21 for the the value of x. Assuming no any knowledge about algebra means that there is no "program" or method for solving this type of problem. What could be done? • Let assume we do know how to evaluate an arithmetic expression. Consequently, we can compute the value of an expression like 4(10)-3=37. Given this ability to evaluate arithmetical expressions, we can then use that ability to specify a test. That is, we can see if 37 equal 21. Since it does not, we can conclude that 10 is not the solution to this problem. Non-Intelligent Search Methods Also known as WEAK (low level adaptive ) Search Methods BLIND SEARCH cont • Now, we also need the ability to generate some other candidate solutions. In this case this ability to generate candidate solutions can be achieved by simply being able to generate a number,...any number. So we need some sort of rule that generates a number and then we may test that number to see if it is a solution. If the test succeeds, then procedure stops and solution is in hand. If not, procedure simply generates another number, and so on. • This is what is known as blind search. It is blind in the sense that there is not only no knowledge that is being used to direct the search, but also in the sense that the search is unsystematic. 7 Non-Intelligent Search Methods Also known as WEAK (low level adaptive ) Search Methods GRAPH SEARCH • Blind search is an unsystematic and inefficient procedure. To increase its efficiency various ways to control the generation of alternatives must be explored. In addition to a set of generators and ways of testing the candidates generated, control structure is also needed. • Different types of control can be defined and visualized if the various candidates generated are represented as nodes in a search graph. In such a state space search each node may be considered to represent a legal state, i.e. such a representation should provide that a state is consistent "axiomatization" of the point within the domain. • In graph representation a link from a node N (state) to another node M (state) denotes the fact that the application of some generator to state N maps this state into state M. In this case state M is said to be directly reachable from state N. There may be many states other than M that are directly reachable from N. Non-Intelligent Search Methods Also known as WEAK (low level adaptive ) Search Methods GRAPH SEARCH cont • The number of such states is often referred to as the branching factor. Graphically, branching is represented by a set of links going from N to the set of directly reachable states {M1, M2, ...Mi,...,Mj}. N Mi M1 M2 Mj 8 Non-Intelligent Search Methods Also known as WEAK (low level adaptive ) Search Methods GRAPH SEARCH cont • It is typically the case that many different states can be reached from each of the states Mi. For example, state L1 may be directly reachable from M1, and so on. In this case it is said that L1 is reachable from N since there exists a path of states from N to L1. However, there may be more than one path from N to L1 and there may be many ways to reach L1 that do not go through N. N Mi M1 M2 Mj L1 Non-Intelligent Search Methods Also known as WEAK (low level adaptive ) Search Methods GRAPH SEARCH cont • A systematic search method is the one that efficiently organizes the generation and search for such paths. • Since state space search typically begins with the start state/node and proceeds via generation of reachable states from this start state, the representation of the space searched at any point in the process has the appearance of a tree. • It is not technically a tree of states unless the search method includes within it a procedure, which insures that any cycle, i.e. a path to a state already reached on that path, is eliminated. 9 Non-Intelligent Search Methods EXHAUSTIVE SEARCH METHODS SEARCH TREE • The hypothetical search tree is given at Fig. 1.The root node is labeled s0 to indicate the starting state for search. The node g in the center is three levels down and is considered as the goal node. Non-Intelligent Search Methods EXHAUSTIVE SEARCH METHODS SEARCH TREE • For purposes of illustration, it may be assumed that search tree is the total search tree. This is, of course, unrealistic because most search trees have hundreds if not millions of nodes. In this example tree there are only 23 nodes or 22 states that can be reached from the start node; the branching factor from any node varies between one and three, and the goal node is only three moves away from the start node. 10 Non-Intelligent Search Methods EXHAUSTIVE SEARCH METHODS • The search proceeds by generating and testing each node that is reachable from a parent node before it expands any of these children. • Search is terminated when a solution is found, i.e. the test of a state returns true. • The solution is traced back to the starting node. • Note that all the paths that were searched but were not part of the solution should be held in a memory until the goal node is reached. In this example, 18 nodes were opened before the solution was found. Breadth-First Search Non-Intelligent Search Methods EXHAUSTIVE SEARCH METHODS • …is said to be exhaustive because the search guarantees to generate all reachable states before terminates with failure. By generating the all of the nodes at a particular level before proceeding to the next level of the tree (the Breadth-First Strategy) this control regime guarantees that the space of possible moves will be systematically examined. • Logical assumption is that the space that will be searched is finite. Of course, it could be quite large and the solution may lay a thousand steps away from the start node. Consequently, in practice one would usually specify a depth or move distance from the starting node beyond which search would not continue. In given example the maximum depth was only three so there was no problem. • …requires considerable memory resources. It does, however, guarantee that if there is a solution it will be the shortest possible. Breadth-First Search 11 Non-Intelligent Search Methods EXHAUSTIVE SEARCH METHODS • The tree is examined to some depth d before another path is considered. • In this example, the maximum depth of the tree is only 3 so when this limit is reached and if the solution has not been found, the search would backtrack to the previous level and explore any remaining alternatives at this level, and so on. • It is this systematic backtracking procedure that guarantees that it will systematically and exhaustively examine all of the possibilities. Depth-First Search Non-Intelligent Search Methods EXHAUSTIVE SEARCH METHODS • Only 14 nodes should be explored before the solution is found and there is no never need to hold more than three nodes in memory. However, the solution might have been the first node generated on the far right of the tree. In this case, the Breadth-first procedure would have found the solution much more quickly than the Depth-first procedure. • Again, if the tree is very deep and the maximum depth searched is less that the maximum depth of the tree, then this procedure is "exhaustive" modulo the depth that has been set. Depth-First Search 12 Non-Intelligent Search Methods EXHAUSTIVE SEARCH METHODS • This is an exhaustive search procedure that is a blend of depth first and breadth first search. • It works so that it first performs a depth-first search to depth one, then discard the nodes generated in the first search, start over and do a depth-first search to level two, three ... until the goal state is reached. Depth-First Iterative-Deepening Non-Intelligent Search Methods EXHAUSTIVE SEARCH METHODS • This search is not necessarily exhaustive. Being a kind of heuristic search, hillclimbing is provided with an evaluation function that can be used to estimate the distance from the current state to the goal state. If these estimates are computed at each choice point, then they can be used as a basis for choice. If the estimates are reasonably related to the actual distance, then this heuristic typically allows finding a solution while selectively (rather than exhaustively) searching the space of possible moves. • In hill climbing, the control regime is basically that of depth-first search. However, at each choice point in the depth-first search all of the candidates are generated and then evaluated using the evaluation function. The result of this evaluation informs about which of these alternatives to pursue. Hill-climbing 13 Intelligent Search Methods NEIGHBORHOOD SEARCH … is typical for combinatorial optimization problems is that within the domain S of the cost function f, a subset of S designated as N(x) may be associated with each point xεS. • N(x) is usually referred to as the neighborhood of x. • At each step, search method proceeds from the current point x by considering a point or a set of points in N(x). • The common ingredients of a neighborhood based search technique are shown in Fig. 2. Intelligent Search Methods NEIGHBORHOOD SEARCH Neighborhood-based search algorithms are differentiated from each other by the way in which the considered points are accepted via some selection criterion based on cost function evaluations. Can be grouped into two main categories: GLOBAL SEARCH TECHNIQUES May be more or less considered as intelligent. They are, without exception, stochastic optimization methods. LOCAL SEARCH TECHNIQUES May be considered as weak, i.e. low-intelligent optimization methods NEIGHBORHOOD SEARCH 14 Intelligent Search Methods NEIGHBORHOOD SEARCH … Good example are the steepest descent algorithms that move from point to neighboring point accepting each successive point as a solution only if it has a lower cost than the current solution. … This causes entrapment in the point with the lowest cost function in the neighborhood. Therefore - a local optimum is obtained. LOCAL SEARCH TECHNIQUES (weak, low-intelligent optimization methods) Intelligent Search Methods RANDOM WALK The simplest of the neighborhood-based search techniques. At each step, a point x'εN(x) is generated from the current point x. If x' has a cost function value not greater than the current point, i.e. f(x')f(x), the point x' is accepted with a probability p - which is the probability of accepting uphill moves. Varying the parameter p from 0 to 1 a whole family of Random Walk algorithms that range from greedy search (p=0) to purely random search (p=1) may be created. In several problems of interest it may be expected that since greedy search will result in the search converging to a local minimum, a small non-zero uphill probability can help in escaping such minima. When the uphill probability is too high, however, the search becomes more random and there may be a performance drop due to this. For some problems, a greedy search repeated from different starting points may be utilized to increase the possibility of locating near-optimal solutions. This is known as multiple-start local search (MLS) method. 15 Intelligent Search Methods RANDOM WALK The simplest of all random walk algorithms is the random search. The algorithm iteratively generates a random solution and evaluates this solution. As it precedes it keeps a record of the best solution found so far. The process continues for a preset number of iterations or until a good enough solution is found. Intelligent Search Methods STOCHASTIC HILL-CLIMBING The most straightforward of all effective stochastic search algorithms are so called hill-climbers. They are the most appropriate class of intelligent search strategies to be mentioned before proceed with other techniques of stochastic optimization. All hill-climbers work on a two-step iterative process. They first generate a new solution by mutating a copy of the best solution found so far. Then decide whether or not to keep the new solution or the old solution as the best so far. • This process is iterated until either a good enough solution is found or a given time has elapsed. • So, all hill-climbers keep at most two solutions to the problem in memory at any one instance time, and the difference between hill-climbers is largely in the (mutation) operator that is used to generate the new solutions from the old. 16 Intelligent Search Methods NEIGHBORHOOD SEARCH They provide an escape from local optima by providing the ability to selectively accept successive points even if they have a higher cost than the current solution. The most popular are four classes of algorithms: Random Walk (RW) Simulated Annealing (SA) Tabu Search (TS) Genetic Algorithm (GA) These algorithms are reasonably simple and easy to implement and that is perhaps the chief reason for their popularity. They are very general and robust in their simplest form assuming nothing about the structure of the space being searched. To improve their efficiency when applied to specific problems, they may be modified suitably via better neighborhood definitions or hybridized with heuristic techniques. GLOBAL SEARCH TECHNIQUES Intelligent stochastic optimization methods The End HEURISTIC AND METAHEURISTIC TECHNIQUES	Translation - Serbian Univerzitet Novi Sad, Poljoprivredni fakultet Univerzitet Beograd, Poljoprivredni fakultet Studijski program za zajedničku master diplomu: MENADŽMENT VODA U POLJOPRIVREDI ( LOLAqua) Tema: NAPREDNE SISTEMSKE ANALIZE VODENIH RESURSA HEURISTIČKE I METAHEURISTIČKE TEHNIKE PREGLED USLOVI Heuristika je skup jednostavnih pravila ili tehnika koje poboljšavaju učinak prosečnog slučaja u rešavanju problema. U kontekstu optimizacije, heuristika će pronaći dobra rešenja za teške probleme, često relativno brzo. Metaheuristika je „heuristika za rukovođenje heuristikom“. Metaheuristika kontroliše aplikacije i interakciju jedne ili više heuristika tražeći bolje rešenje od onog koje bi svaka zasebna heuristika samostalno pronašla. Jasna razlika između heuristike i metaheuristike u suštini ne postoji. KOMBINATORIJALNA OPTIMIZACIJA (KO) • Jak odnos između H/MH i KO. • KO je potraga za globalnim optimumom kroz velike, veoma velike i „ beskonačno velike“ diskretne domene rešenja. • U stvari, svaka optimizaciona procedura pokušava da nađe optimalno rešenje x* = opt{f(x)\|xεS}, gde je f tražena funkcija a S njen domen tj. set mogućih rešenja datog problema. KO problem je optimizacioni problem gde S ima diskretne članove. • Operator ’opt’ predstavlja ili ’minimizaciju’ ili ’maksimizaciju’. Radi jednostavnosti, nadalje će predstavljati minimizaciju. • Iz toga sledi da će se tražena funkcija zvati funkcija troška. Prelaz na maksimizaciju može se jednostavno učiniti promenom znaka funkcije i time promeniti njeno ime u funkciju dobiti. KOMBINATORIJALNA OPTIMIZACIJA (KO) • Kada je kompleksnost problema niska u cilju pronalaska rešenja mogu se koristiti ili iscrpna pretraga prostora ili deterministički algoritmi. • Postoje mnogi deterministički algoritmi i grupirani su u različite kategorije, kao što su: Linearno programiranje, Nelinearno programiranje, Dinamičko programiranje ili Granavanje-i-Ograničenje ( Branch-and-Bound) • Za složenije probleme, heurističke, metaheurističke i stohastičke tehnike pretrage su se pokazale kao jedine tehnike kojima se može pronaći optimalna ili približno optimalna tačka u velikom prostoru rešenja. Ove tehnike se obično grupišu pod terminom Inteligentne metode pretrage i mnogo više su tema Veštačke inteligencije nego klasičnih teorija optimizacije. H/MH ALGORITMI Velika klasa KO algoritama se naziva opšti iterativni algoritmi, znana i kao klasa aproksimativnih algoritama. Karakteristike opštost jednostavnost implementacije robusnost H/MH ALGORITMI Dizajniranje Dizajn h/m algoritama pretpostavlja inkorporaciju nekih od sledećih tehnika i osobina; • Tehnike „ uspon na brdo“ koje umeju da traže bolja rešenja kada je inicijalno rešenje dato kao polazna tačka. • Heuristika iterativnog poboljšanja, kao što su smanjenje uskog grla, koje može da se upotrebi za poboljšanje rešenja kroz ponovljenu aplikaciju. • Metode pretrage bazirane na znanju koje se mogu upotrebiti kada je važna jedna ili više karakteristika potencijalnog rešenja H/MH ALGORITMI Dizajniranje ( nastavak) • Algoritmi bazirani na principima biološke evolucije poznati su kao evolucionarno izračunavanje. Ovi algoritmi, kao što su genetski algoritmi, su moćne metode rešavanja problema kod kojih populacija potencijalnih rešenja „evoluira“, postajući bolja i bolja po principu „opstanka najjačih“. • Tehnike zasnovane na principima termodinamike, koje imitiraju način zamrzavanja i kristalizacije tečnosti ili hlađenja i kaljenja metala. Algoritmi kao što su simulirano kaljenje vode pretragu za dobrim rešenjem dozvoljavajući prihvatanje lošijih rešenja zbog vrednosti „trenutne temperature“ definisane „ šemom hlađenja“ onog koji vrši kaljenje. • Procedure bazirane na privremenoj eliminaciji pretrage unatražnog praćenja, kao što je tabu pretraga. H/MH ALGORITMI KONSTRUISANJE Najčešći način je: • Kombinovanje različitih elemenata rukovođene slučajne pretrage i iterativnog poboljšanja. • Fino podešavanje rezultata sa ciljem zadovoljenja zahteva ciljane aplikacije. Ovakvi zahtevi često uključuju ograničenja performanse i memorije. H/MH ALGORITMI NAJČEŠĆE KORIŠTENI ALGORITMI Genetski algoritmi (GA) Simulirano Kaljenje (SK) Tabu Pretraga (TP) H/MH ALGORITMI Genetski algoritmi (GA) Simulirano Kaljenje (SK) Tabu Pretraga (TP) Sve spadaju u optimizacione heuristike i imaju nekoliko međusobnih sličnosti: • Sve su aproksimativni ( heuristički) algoritmi, npr. ne garantuju nalaženje optimalnog rešenja. • Slepe su, u smislu da ne znaju kada je pronađeno optimalno rešenje. Mora im se reći kada da stanu. • Sve imaju sposobnost „ penjanja uz brdo“, npr. ponekad prihvataju uzbrdne ( loše ) poteze. • Sve su opšteg karaktera i mogu se lako kreirati za implementaciju u ma kom problemu kombinatorijalne optimizacije; sve što je potrebno je da imamo odgovarajuću reprezentaciju rešenja, funkciju troška i mehanizam prelaza pretražnog prostora. • Asimptotski dolaze do optimalnog rešenja, pod određenim uslovima. • Robusne su u potrazi za najboljim rešenjem; genetski algoritam pokazuje i paralelnu strategiju pretrage. NEINTELIGENTNE METODE PRETRAGE Poznate i pod nazivom SLABE ( niske prilagodljivosti ) metode pretrage SLEPA PRETRAGA • Logika koja stoji iza ove tehnike je generiši i testiraj, što znači generisanje potencijalnih rešenja određenog problema, a zatim i testiranje da li je potencijalno rešenje u stvari i rešenje problema. • Na primer: pronađi x u 4x-3 = 21. Pretpostavimo da ne postoji nikakvo znanje o algebri i stoga ne postoji ni „program“ ili metod za rešavanje ovog tipa problema. Šta se može učiniti? • Pretpostavimo da znamo kako da vrednujemo aritmetički izraz. Stoga, možemo izračunati vrednost izraza kao 4(10)- 3 = 37. Pošto imamo sposobnost vrednovanja aritmetičkog izraza možemo je iskoristiti za specificiranje testa, tj da proverimo da li je 37 jednako 21. Pošto nije, možemo zaključiti da 10 nije rešenje. NEINTELIGENTNE METODE PRETRAGE Poznate i pod nazivom SLABE ( niske prilagodljivosti ) metode pretrage SLEPA PRETRAGA (nastavak) • Sada nam treba sposobnost generisanja nekih drugih potencijalnih rešenja. U ovom slučaju se sposobnost generisanja potencijalnih rešenja ogleda u sposobnosti generisanja nekog broja ..., ma kog broja. Dakle, treba nam neka vrsta pravila koje generiše broj koji ćemo kasnije testirati da bismo saznali da li je on i rešenje problema. Ako test uspe, procedura se zaustavlja i rešenje je pronađeno. Ako ne, procedura generiše sledeći broj, i tako dalje. • Ovo je poznato kao slepa pretraga. Slepa u smislu da ne samo da ne postoji određeno znanje koje se koristi za rukovođenje pretragom već i u tome da je sama pretraga nesistematska. NEINTELIGENTNE METODE PRETRAGE Poznate i pod nazivom SLABE ( niske prilagodljivosti ) metode pretrage GRAFIČKA PRETRAGA • Slepa pretraga je nesistematska i neefikasna procedura. Da bi se povećala njena efikasnost moraju se istražiti različite kontrole generisanja alternativa. Kao dodatak grupi generatora i načina testiranja generisanih potencijalnih rešenja potrebna je i kontrolna struktura. • Mogu se definisati i vizualizirati različiti tipovi kontrole, ukoliko se različita generisana potencijalna rešenja predstave kao čvorovi u grafikonu pretrage. U takvoj pretrazi prostora stanja svaki čvor se može smatrati predstavnikom legalnog stanja. Na primer, takva prezentacija trebalo bi da obezbedi to da je određeno stanje nepromenljiva „ aksiomatizacija“ tačke unutar domena. • Kod grafičke reprezentacije, link od čvora N (stanja) do drugog čvora M (stanja) označeva činjenicu da primena određenog generatora na stanje N povezuje stanje N sa stanjem M. U ovom slučaju kaže se da je stanje M direktno dostupno iz stanja N. Moguća su i brojna druga stanja drugačija od M koja su direktno dostupna iz stanja N. NEINTELIGENTNE METODE PRETRAGE Poznate i pod nazivom SLABE ( niske prilagodljivosti ) metode pretrage GRAFIČKA PRETRAGA (nastavak) • Broj takvih stanja se često naziva faktorom grananja. Grafički, grananje je predstavljeno setom linkova koji idu od stanja N do direktno dostupnih stanja { M1, M2, ... Mi,..., Mj}. NEINTELIGENTNE METODE PRETRAGE Poznate i pod nazivom SLABE ( niske prilagodljivosti ) metode pretrage GRAFIČKA PRETRAGA (nastavak) • Tipičan je slučaj da su mnoga različita stanja dostupna iz svakog od stanja Mi. Na primer, stanje L1 je direktno dostupno iz stanja M1, i tako dalje. U ovom slučaju kažemo da je stanje L1 dostupno iz stanja N jer postoji niz povezanih stanja od N do L1. Ipak, moguće je da postoji više od jedne putanje od N do L1, kao i da postoje mnogi načini dolaska do L1 koji ne idu preko N. NEINTELIGENTNE METODE PRETRAGE Poznate i pod nazivom SLABE ( niske prilagodljivosti ) metode pretrage GRAFIČKA PRETRAGA (nastavak) • Sistematska metoda pretrage je ona koja efikasno organizuje generisanje i pretragu takvih putanja. • Pošto pretraga prostora stanja obično počinje od startnog stanja/čvora i nastavlja se generisanjem stanja koja su dostupna iz tog startnog stanja, prikaz prostora pretraženog u bilo kojoj tačci pretrage ima izgled stabla. • Tehnički, to nije stablo stanja ukoliko metoda pretrage ne uključuje i proceduru koja osigurava da je bilo koji ciklus (na primer, putanja do stanja koje je već dostignuto na toj putanji) eliminisan. NEINTELIGENTNE METODE PRETRAGE ISCRPNE METODE PRETRAGE STABLO PRETRAGE • Hipotetičko stablo pretrage prikazano je na grafikonu 1. Korenski čvor je označen kao s0 da bi označio startno stanje pretrage. Čvor g u sredini grafikona je tri nivoa niže i predstavlja ciljni čvor. Grafikon 1 Stablo pretrage NEINTELIGENTNE METODE PRETRAGE ISCRPNE METODE PRETRAGE STABLO PRETRAGE • Radi ilustracije, možemo pretpostaviti da je stablo pretrage i konačno stablo pretrage. Ovo je naravno nerealno jer većina stabala pretrage ima stotine ako ne i milione čvorova. U našem primeru postoje samo 23 čvora odnosno 22 stanja dostupna iz startnog stanja; faktor grananja ma kog čvora varira od jedan do tri, a ciljni čvor je samo tri pokreta dalje od startnog. Grafikon 1 Stablo pretrage NEINTELIGENTNE METODE PRETRAGE ISCRPNE METODE PRETRAGE Pretraga „ prvo u širinu“ • Pretraga se nastavlja generisanjem i testiranjem svakog čvora koji je dostupan iz čvora roditelja( parent node) pre nego što se proširi na čvor dete (child node). • Pretraga se prekida kada je rešenje pronađeno, tj. test stanja se pokazao kao istinit. • Rešenje vodi poreklo iz startnog čvora. • Obratite pažnju da i sve putanje koje su pretražene, ali nisu deo rešenja, trebaju biti memorisane dok se ne stigne do ciljnog čvora. U našem primeru, 18 čvorova je otvoreno pre nego što je pronađeno rešenje. NEINTELIGENTNE METODE PRETRAGE ISCRPNE METODE PRETRAGE Pretraga „ prvo u širinu“ • Za ovu pretragu se kaže da je iscrpna jer garantuje generisanje svih dostupnih stanja pre nego što proglasi neuspeh. Generisanjem svih čvorova na određenom nivou pre nego što pređe na sledeći nivo stabla ( „ prvo u širinu“ strategija) ovaj režim kontrole garantuje da će prostor mogućih pokreta biti sistematski ispitan. • Logična pretpostavka je da je prostor koji se pretražuje konačan. Naravno, on može biti prilično velik i rešenje može biti pronađeno hiljadama koraka dalje od startnog čvora. Stoga, obično ćemo u praksi odrediti dubinu ili udaljenost od startnog čvora iza koje se pretraga neće nastaviti. U našem primeru, maksimalna dubina je bila tri i nije bilo nikakvih problema. • Ova pretraga zahteva znatne memorijske kapacitete. Sa druge strane, ona garantuje da će, ukoliko rešenje postoji, biti najkraća moguća. NEINTELIGENTNE METODE PRETRAGE ISCRPNE METODE PRETRAGE Pretraga „ prvo u dubinu“ • Stablo se ispituje do određene dubine d pre nego što se druga putanja uzme u obzir. • U ovom primeru, maksimalna dubina stabla je samo tri i kada se dostigne ovaj limit, a ne pronađe rešenje, pretraga se vraća unazad na prethodni nivo i ispituje preostale alternative na njemu, i tako dalje. • Upravo ta sistematska procedura pretrage unazad garantuje sistematsko i iscrpno ispitivanje svih mogućnosti. NEINTELIGENTNE METODE PRETRAGE ISCRPNE METODE PRETRAGE Pretraga „ prvo u dubinu“ • Samo 14 čvorova treba istražiti pre pronalaska rešenja i nikada ne postoji potreba za pamćenjem više od tri čvora. Ipak, rešenje je moglo biti i prvi čvor generisan u krajnjem desnom delu stabla. U tom slučaju bi pretraga „prvo u širinu“ pronašla rešenje daleko pre nego pretraga „prvo u dubinu“. • Još jednom, ako je stablo jako duboko i maksimalna pretražena dubina manja od maksimalne dubine stabla tada je ova procedura „ iscrpna“ za modul dubine za koji je podešena. NEINTELIGENTNE METODE PRETRAGE ISCRPNE METODE PRETRAGE Iterativno produbljivanje pretrage „prvo u dubinu“ • Ova iscrpna procedura pretrage je spoj pretraga „prvo u dubinu“ i „prvo u širinu“. • Funkcioniše na taj način da prvo vrši pretragu „prvo u dubinu“ do nivoa jedan, zatim odbacuje čvorove generisane u prvoj pretrazi, počinje ispočetka i vrši pretragu „prvo u dubinu“ za nivo dva, tri ... dok se ne dostigne ciljano stanje. NEINTELIGENTNE METODE PRETRAGE ISCRPNE METODE PRETRAGE „Uspon na brdo“ • Ova pretraga nije uvek iscrpna. Pošto spada u vrstu heurističke pretrage, „uspon na brdo“ sadrži evaluacionu funkciju koja se može upotrebiti za procenu razdaljine između trenutnog stanja i ciljnog stanja. Ako se ove procene izračunavaju na svakoj izbornoj tačci onda se mogu upotrebiti kao osnova izbora. Ako su procene u razumnoj meri povezane sa stvarnom razdaljinom tada ova heuristika tipično dozvoljava pronalazak rešenja selektivno ( više nego iscrpno) pretražujući prostor mogućih pokreta. • Kod „uspona na brdo“, kontrolni režim je u suštini isti kao i kod pretrage „prvo u dubinu“. Ipak, u svakoj izbornoj tačci pretrage „prvo u dubinu“ sva potencijalna rešenja su generisana i evaluirana koristeći evaluacionu funkciju. Rezultat ove evaluacije informiše nas o tome koju od alternativa da sledimo. INTELIGENTNE METODE PRETRAGE PRETRAGA NEPOSREDNOG OKRUŽENJA ( KOMŠILUKA) Ono što je tipično za probleme kombinatorijalne optimizacije je da, unutar domena S funkcije troška f, postoji određeni podskup, označen sa N(x), koji se može povezati sa svakom tačkom xεS. • N(x) se obično karakteriše kao neposredno okruženje x. • U svakom koraku, metoda pretrage nastavlja od tekuće tačke x razmatrajući pojedinačnu ili set tačaka u N(x). • Zajednički sastojci tehnike pretrage bazirane na neposrednom okruženju su prikazani na grafikonu 2. Grafikon 2 Algoritam opšte pretrage bazirane na neposrednom okruženju INTELIGENTNE METODE PRETRAGE PRETRAGA NEPOSREDNOG OKRUŽENJA ( KOMŠILUKA) Algoritmi bazirani na pretrazi neposrednog okruženja se međusobno razlikuju u načinu na koji su razmatrane tačke prihvaćene preko određenih selekcionih kriterijuma baziranih na evaluacijama funkcije troška. Na osnovu toga, mogu se grupisati u dve glavne kategorije: TEHNIKE LOKALNE PRETRAGE Mogu se smatrati slabim tehnikama, tj. nisko inteligentnim metodama optimizacije. TEHNIKE GLOBALNE PRETRAGE Mogu se manje-više smatrati inteligentnim tehnikama. One su, bez izuzetka, stohastičke metode optimizacije. INTELIGENTNE METODE PRETRAGE PRETRAGA NEPOSREDNOG OKRUŽENJA ( KOMŠILUKA) TEHNIKE LOKALNE PRETRAGE ( slabe, niskointeligentne metode optimizacije) ... Dobar primer su algoritmi najstrmijeg pada koji se kreću od tačke do susedne tačke prihvatajući svaku sukcesivnu tačku kao rešenje jedino ako ona predstavlja manji trošak od trenutnog rešenja. ... To izaziva zaglavljivanje u tačci sa najnižom funkcijom troška u okruženju. Stoga – pronađen je lokalni optimum. INTELIGENTNE METODE PRETRAGE NASUMIČNA ŠETNJA • Najprostija tehnika bazirana na pretrazi neposrednog okruženja • Pri svakom koraku, tačka x’εN(x) je generisana iz trenutne tačke x. • Ako x’ ima funkciju troška koja nije veća od trenutne tačke, tj f(x’)f(x), tačka x’ se prihvata sa verovatnoćom p, što predstavlja verovatnoću prihvatanja uzbrdnih poteza. • Menjajući parametar p od 0 do1 može se kreirati citava porodica algoritama nasumične šetnje koja se proteže od pohlepne pretrage (p=0) do čisto nasumične pretrage (p=1). • Kod nekoliko problema od interesa može se očekivati da će, zbog verovatnoće da će pohlepna pretraga dovesti do usmeravanja same pretrage ka lokalnom minimumu, mala uzbrdna verovatnoća različita od nule pomoći u izbegavanju takvog minimuma. Sa druge strane, u slučaju da je uzbrdna verovatnoća prevelika, sama pretraga postaje nasumičnija i zbog toga može doći do pada performanse. • Kod nekih problema, pohlepna pretraga ponovljena iz različitih startnih tačaka može dovesti do povećanja verovatnoće pronalaska skoro-optimalnih rešenja. Ovo je poznato kao metod lokalne pretrage sa višestrukim startom (MLS). INTELIGENTNE METODE PRETRAGE NASUMIČNA ŠETNJA • Najprostiji od svih algoritama nasumične šetnje je nasumična pretraga. • Algoritam iterativno generiše nasumična rešenja i evaluira ih. • Kako nastavlja, beleži najbolja rešenja pronađena do tog trenutka. • Proces se nastavlja za unapred određeni broj ponavljanja ili dok nije pronađeno dovoljno dobro rešenje. Grafikon 3 Algoritam nasumične šetnje INTELIGENTNE METODE PRETRAGE STOHASTIČKI USPON NA BRDO • Najdirektniji od svih efektivnih algoritama stohastičke pretrage se zovu penjači na brdo. • Najprikladnija su klasa strategija inteligentne pretrage koju treba spomenuti pre prelaska na druge tehnike stohastičke optimizacije. • Svi penjači na brdo rade na principu dvokoračnog iterativnog procesa. Oni prvo generišu novo rešenje mutirajući kopiju najboljeg do tada pronađenog rešenja. Zatim odučuju da li da zadrže novo rešenje ili ono koje je do tada bilo najbolje. • Proces se ponavlja dok se ne pronađe dovoljno dobro rešenje, ili dok ne istekne vreme zadato za pronalazak rešenja. • Dakle, svi penjači na brdo, u bilo kom trrenutku, zadržavaju najviše dva rešenja problema u svojoj memoriji i razlika između samih penjača uz brdo je najviše u mutacionom operatoru koji se koristi za generisanje novih rešenja iz postojećeg starog. INTELIGENTNE METODE PRETRAGE PRETRAGA NEPOSREDNOG OKRUŽENJA ( KOMŠILUKA) TEHNIKE GLOBALNE PRETRAGE Metode inteligentne stohastičke optimatizacije • Omogućavaju izlaz iz lokalnog optimuma obezbeđujući sposobnost selektivnog prihvatanja sukcesivnih tačaka čak i kada one predstavljaju veći trošak od trenutnih rešenja. • Najpopularnije su četiri klase algoritama: Naumična šetnja ( NŠ) Simulirano kaljenje (SK) Tabu pretraga (TP) Genetski algoritmi (GA) • Ovi algoritmi su dovoljno jednostavni i laki za primenu i to je možda i najvažniji razlog njihove popularnosti • Veoma su opšti i robusni u svojoj najjednostavnijoj formi i ne vrše nikakve pretpostavke o strukturi prostora koji se pretražuje. • Da bi im se popravila efikasnost kod specifičnih problema, na odgovarajući način se mogu modifikovati uz pomoć boljih definicija neposrednog okruženja, ili hibridizacijom sa heurističkim tehnikama.
English to Serbian: science and its images General field: Social Sciences Detailed field: Social Science, Sociology, Ethics, etc.
Source text - English Science and it’s Images – Promise and Threat: From Classic Literature to Contemporary Students’ Images of Science and “The Scientist” PAZIT KOREN VARDA BAR Hebrew University of Jerusalem ABSTRACT: The physical and social image of the scientist among school children, student teachers, and teachers over the last 50 years was investigated. Interest has also been shown in the perception of the personality behind the physical stereotype. Nevertheless, the value judgments of science and scientists and the positive and negative mind-sets attaching to these judgments in our society were less investigated, either in Israel or abroad. In this investigation models given to science and scientists in the classical literature and by some popular science writers were investigated, together with contemporary learners’ views. The populations consist of 125 high school students from Israel. Several tools were used during this investigation to decipher the images of science: Closed questionnaire, writing an essay, and semi-structured collective interviews. Classical authors have pessimistic views about science and the scientists. Most models are unfavorable, and criticize the scientists: The mad and monstrous scientist (Frankenstein), the scientist who is alienated from human life (The Physicists), the scientist who is cut off from reality, the “geek” (Gulliver’s Travels), the scientist whose irresponsible research is harmful to the environment (Jurassic Park), the scientist who hungers for knowledge at any cost (Faust). The positive images found especially in the popular science literature: the scientist who cures diseases (Microbe Hunters), the scientist who has professional integrity (Galaxies), the scientist who keeps to the rules of the scientific method to obtain objective results (Wrinkles in Time). We found that some expressions relating to fear of science which have appeared in the classics since the beginning of the 18th century were found in a similar way with students of the 21st century, while others expressed that same fear in different ways. There was also an identification with Swift’s “unsociable and unemotional” scientists. Alongside the existence of expressions of fear of science, and mainly ambivalent opinions of students toward science, the dominant picture that stands out in this study is that Interchange, Vol. 40/2, 141–163, 2009. © Springer 2009 DOI: 10.1007/s10780-009-9088-1 142 PAZIT KOREN and VARDA BAR our contemporary students are pro science, and regard science as a useful area of society. KEYWORDS: Scientist’s image, models of science and scientists, scientific concepts, attitudes towards science, high school students, Israel, classical literature, popular books, ethics, moral judgment, dilemma. Introduction The influence of science on economic, societal, and technological advances is a fait accompli. Many images adhere to this very influential field: Sentiments, evaluations, and even fantasies and wishes. Some of them are positive: Science expresses the spirit of progress, develops the human spirit, improves and extends our lives, others expressing suspicion and fear, apprehension of the capacity of science to seed devastation and calamity. These emotionally loaded views which accompany science, and especially the sub-currents of the critical relations between science and society, are not always expressed openly in social and public conversations, but often find expression in the arts, such as in literature, theater, and film: The classical creation is not a mirror or an exact reflection of what is understood and known to all; it allows for the fears which are not expressed openly, if at all, to be exposed, because of the possibility to challenge the taboo and the moral judgments acceptable in society. (Haynes, 1994, p. 313) The values applied to science and scientists which appear in the classical literature, have hardly ever been examined among contemporary students – over time, some of these images have developed into social myths and symbols, leaving their mark on our attitude toward science. For this reason this study is important. During the last 50 years, many studies were carried out on school children, student teachers, and teachers regarding their perception of the professional and social image of scientists. The innovation in this study is its in-depth examination of the attitude of students towards science and scientists, in other words their value judgments of science and scientists and the positive and negative mind-sets attaching to these judgments in our society. In order to anchor the work in a wide, cultural reference, and as an origin for the critical evaluation of students, we have sampled a few SCIENCE AND IT’S IMAGES 143 popular science books and classical works dating from the beginning of the 18th century until the second half of the 20th century. We evaluated whether the images of students toward science and the scientist and the emotional relation which accompanies these images found in the study, reflect the views of classical creators throughout history, and whether it is possible to find in the students’ opinions, side by side with the positive ethos, the critical evaluation or sentiments of doubt towards science. As far as we know, an in-depth study using examples taken from the classical and popular literature as is done here has not yet been carried out either in Israel or abroad. This study has the following aims: 1. To present the scientists’ images held by students from two aspects: Typical characteristics of the scientist and the overall image of him or her. 2. To examine the degree to which the students identified with models of science and scientists which appears in some popular science writers and by some classical literature. 3. To examine whether a similarity exists between the values attributed to science and scientists by contemporary students and those portrayed in classical works (see discussion). Theoretical Background Differences That Exist Between Scientific Concepts and Attitudes Towards Science The term scientific concepts relates to the conceptions or misconceptions of children of various ages towards natural scientific domains (Nussbaum & Novick, 1982), such as the concept of living creatures (Koren & Bar, 2007). Concept is a mental model of objects, sensations, or emotions as they are reflected in each individual’s mind. This picture functions for them as a basis for an organized, quite reasonable view of the world around them. Research findings showed that children do not reach formal education, through tabula rosa but rather through previous perceptions. Studies show that children’s (before their formal learning) do not always accord with conventional scientists’ concepts. Their concepts change gradually to become closer to the accepted scientific conceptions. The significant learning of scientific concepts and conceptions is a process of conceptual change wherein the learner substitutes his previous conceptions with scientifically acceptable ones. 144 PAZIT KOREN and VARDA BAR The term attitudes to science refers to extrinsic judgment of science and scientists from the point of view of ethics, aesthetics, the emotions they evoke, their utility or harmfulness, and their affect on society, environment, and family. The attitude to science can take place in any value framework, which accepts or denies scientific validity. An examination of attitudes toward science shows that the scientific theories and images are not judged only according to their verification by logic, but also according to the way in which they influence individuals or society. Therefore, judgment of science is a combination of logic (facts, rules, and theories), values (beliefs), and feelings (motivation) which are an inseparable part of the values of the individual. A number of researchers stand behind the fact that science education does not prepare children properly to function in a responsible way in a society influenced by science and technology. Therefore, it is upon the educational programs to add to acquiring the scientific knowledge and the development of skills, values, and opinions (American Association for the Advancement of Science, 1993). The current study evaluates the value judgments attributed to scientists, as they are reflected in the literature and among contemporary learners. Classical works are used as the origins of the evaluation. According to the opinion of Putnam (2002), there has never been a clear dichotomy between objectivity (scientific concepts and the perception of science) and subjectivity (the attitude towards science) and this is problematic, since many descriptions of human endeavor are both factual and given in terms of values. Previous Studies Dealing with the Images of Science and Scientists The Physical and Social Image of the Scientist Mead and Metraux (1957) discovered that the popular perception of the scientist among high school students was of a white male with facial hair wearing spectacles and a laboratory coat, working alone at a laboratory bench using bottles, test tubes, and chemicals. The above image was widely held in the western world regardless of country or age (Song & Kim, 1999; Spitulnik, Stratford, Krajcik, & Soloway, 1998). This physical image of the scientist appeared also among student teachers in Nigeria (Mbajiorgu & Iloputaife, 2001) and science teachers in India (Rampal, 1992). SCIENCE AND IT’S IMAGES 145 Interest has Been Also Shown in the Perception of the Personality Behind the Physical Stereotype Scientists were generally regarded among college students, as men who see themselves as driven to extend the fortunes of knowledge and explain the world around them (Beardslee & O’Dowd 1961). Rampal (1992) found that science teachers in India regarded scientists as possessing extraordinary ability and strength of character strongly committed to discovering the truth. Scientists gave the impression of being very busy people who frequently look lost. They were characterized by “a scientific temperament” that reflects most aspects of their lives. According to our latest research scientists are intellectual, diligent, methodic, and logical (Koren & Bar, in press), an image found also in Lederman, Wade, and Bell (1998). In his book The Demon-Haunted World: Science as a Candle in the Dark, Sagan (1995) describes another stereotypical image of the scientist as follows: One of the stereotyped occupations is science. Scientists are nerds, socially inept, working on incomprehensible subjects that no normal person would find in any way interesting even if he was willing to invest the time required, which, again, no sensible person would. “Get a life,” you might wish to tell them. (p. 362) Recent research emphasized the influence of cultural background on the image of the scientist (Finson, 2003; Fung, 2002; Stefánsson, 2006; and so forth). Sjøberg, (2002) found (among 20 countries) a difference between the scientist’s image held by 12-13 year old pupils in developed and undeveloped countries. In industrialized (developed) countries, the image is rather stereotypical with a certain (but not very high) percentage perceiving a mad scientist. In Israel this image is found only among young pupils, not more than 11 years old. Some pupils in Western countries explicitly say that scientists want to help other people – and actually do it. In undeveloped countries, the scientist is perceived as heroic, brave, and intelligent, helping other people, curing the sick, and improving the standard of living for everybody. Anderson’s (2006) research among pupils in Ghana found that scientists are perceived as helping the poor and underprivileged, they are servants of humanity, and heroes of society. Such aspects are rarely mentioned among pupils in the West (Anderson, 2006; Sjøberg, 2002). The research populations that took part in this investigation can be classified as Western, and from a developed population. As it is brought up in this review, the majority of projects that were conducted concerning the image of the scientist examined his or her 146 PAZIT KOREN and VARDA BAR physical and sociological image. In a number of studies, the students evaluated the characteristics of the scientist, but did not express their point of view towards these characteristics. A thorough examination of the emotional load towards science and scientists as a threat, that affect learners views against their confidence in progress and done among students and compared with the views found in the literature was rarely conducted. It is believed that this study is unique in this regard. Models of Science and the Scientist in Popular Scientific and Classical Literature Although in our culture, the positive ethos of the scientist is widespread, expressions of fear towards science or criticism of its character and its effects on human lives make up a sub-current in the relation towards science. Campbell (2003) claims that feelings of fear towards science are partially a result of a lack of sufficient scientific literacy of the public. However, they are primarily a result of the way the individual views the world in general, and science in particular. This part of the paper will review multi-valued statements, in relation to science as expressed in classical works. These works were written between the 18th century and the second half of the 20th century. Since most of these classical works criticize science and reflect fear from its consequences, we have chosen to balance them by analyzing scientific images as they are presented in popular science books, which illustrate mainly positive images of science and scientists. Within this paper, we concentrated only on literary works, which decisively influenced perception in our culture. The Positive Images Found in Some Popular Science Literature a) The scientist who cures diseases. Walter Reed, Anthony van Leeuwenhoek, Louis Pasteur, and Robert Koch are portrayed as those who “volunteered only to be useful to mankind and to promote science.” Their task was to fight bacteria, “the cause of frightful epidemics.” In order to determine the cause of the yellow fever epidemic, Reed faced the ethical dilemma of whether to endanger the lives of others (his colleagues) and decided to do so for the sake of mankind (as cited in Microbe Hunters, De Kruif, 1926, Introduction, n.p.). b) The scientist who has professional integrity. Shapley, as can be seen from his book Galaxies (1967) is not interested in prestige, choosing pure scientific truth upon his personal opinions. His greatness lies in his ability to acknowledge the superior theory of his professional colleague SCIENCE AND IT’S IMAGES 147 (Hubble) regarding the structure of the galaxy, his endorsement of the two scientists (Henrietta Leawitt and Annie Cannon) whose finding contradicted his theory and his joy when the scientific picture was revealed. c) The scientist who keeps the rules of scientific method to obtain objective and accurate results. Science, according to Shapley (Galaxies, 1967) and Smoot (Wrinkles in Time, 1995), requires hard and accurate work. Great care should be taken to guard against hasty conclusions, and instruments must be frequently calibrated for reliable results. Despite the weighty responsibility, the scientist finds this interesting and enjoyable. “Deviations from the road (new instruments that must be planned and built) can be interesting in themselves” (p. 1). Images of the Scientist in the Classical Creations Which Criticize Science and the Scientists a) The mad and monstrous scientist. The image of the mad scientist, such as Dr. Jekyll and Mr. Hyde (Stevenson, 1886/2001), and others, was firmly planted in the public consciousness by the creation of Frankenstein (Shelley, 1818), the scientist with uncontrollable ambition who creates a living creature from body parts. Drunk with success, he ignores the warnings of others and thinks he is a god. The monster he created accuses him regarding the sin of neglect and finally turns against its maker. b) The scientist is alienated from human life. The scientist whose experience of reality is limited to his analytical skills is portrayed in the play The Physicists (Dürrenmatt, 1988). The main characters are three scientists in a hospital for the mentally ill who murder the nurses in a cold-blooded and calculated manner, claiming, like the defendants in the Nuremberg Trials, that “an order is an order.” c) The scientist is cut off from reality – the “geek.” In his famous book Gulliver's travels, Jonathan Swift (1867/1998) speaks out against the members of the Royal Society of Scientists in England, who are portrayed as “different from other humans in their behavior,” (p. 160) indulging in foolish activities, and particularly lacking in social skills. They are preoccupied with “lofty and elevated thoughts to the extent that they are unable to speak and listen to others” (p. 160). d) The scientist whose irresponsible research is harmful to the environment. The illusion that he who utilizes technology has exclusive control over nature spins out of control and descends into chaos. This is how the scientist is portrayed in the Jurassic Park (Crichton, 1990; 148 PAZIT KOREN and VARDA BAR Spielberg, 1993). He is accused of having no imagination as regards the danger inherent in his future discoveries and his ability to control the environment. e) The scientist who hungers for knowledge at any cost. Goethe’s well-known work (Faust, 1818/1995) presents an ambivalent image of the scientist. On the one hand, his intellectual curiosity leads him to explore the boundaries of human knowledge and yearn to expose its power, but on the other hand, he is stupid and arrogant. In order to achieve his goals, he makes an extremely bad bargain with the Devil that involves sorcery and simple trickery. These positive and negative images of science and the scientist formed the basis for the empirical study. Some of them were used as its challenge. Empirical Study Population The population consisted of 125 of high school students in Israel (9th through 12th grade) of both sexes, including religious and secular groups. The population of high school students came from schools known for having high average scores received on the matriculation examination as well as being students with a motivation for studying. The students come from an advanced socioeconomic background and are children of parents with higher education. The decision to focus on this population for the study stemmed from the evident differences in the levels of motivation and responses obtained at the preliminary stages of this work (not reported here), which favored the population with a high level of literacy. The participants were almost equally distributed between males and females and religious or non-religious – most of the results were not different in the varying populations. In the few instances of differences they were mentioned since even minor difference also deserves discussion. Tools and Their Application First, a closed questionnaire was utilized asking the participants to indicate the moral and other characteristics that define the scientist. This questionnaire obtains its validity from a study by Song and Kim (1999), which used it on their populations. Some characteristics which appear in the questionnaire (such as morality judgment) were added in this study. SCIENCE AND IT’S IMAGES 149 Second, a written essay on the scientist’s image was used. This tool was presented in two forms: Without direction, and in a more focused manner, requiring the students to relate to two contrasting images of scientists (Appendix 1). The instructions for writing this essay were given while presenting the task. Third, semi-structured group interviews were conducted among different groups of high school students (each containing 25 students). We examined their attitude toward the image of science and the scientist as portrayed in two creations, one from the popular science literature, the other from the classical literature, and to judge the scientist’s actions from a moral aspect. Group A was faced with the ethical dilemma of Walter Reed mentioned in The Microbe Hunters (De Kruif, 1926) whether to endanger the lives of others (among them his colleagues) in order to determine the cause of the yellow fever epidemic. Group B was interviewed after observing the movie: Frankenstein (Appendix 2). Leading questions were used in this semi-structured group interview. Despite this, most of the findings recorded in the interviews resulted from of free debate between the participants who expressed varied opinions. The Data Analysis The study combines qualitative and quantitative approaches. The main goal of the study is an analysis of the material from the participants’ standpoint. Numerical analysis of the students’ views was done concerning the typical characteristics of the scientist which appeared in the questionnaire (Research tool no. 1). The information produced by essays and interviews (Research tools no. 2 and 3) were qualitatively analyzed around central and sub-central categories. The presentation of the sub-central categories was accompanied by literal responses of the participants. The categorical names were taken from the terminology of the researcher (i.e., primary category: the evaluation of the scientist and the scientific process in the creation. Sub-categories: positive evaluation, negative evaluation, ambivalent evaluation). Findings Characteristics (value-based) and Others (of the scientist) in the Closed Questionnaire Table 1 displays the average numerical grade of each of the scientist properties given by the participants on a scale of 1 to 5. The fifth level 150 PAZIT KOREN and VARDA BAR states a definitive agreement with the given positive characteristic. Level one states the definitive agreement with the opposing negative characteristic, and level three either states a complement between either characteristics, or a lack of knowledge regarding this characteristic. Table One. Characteristics of the Scientist – Numerical Grade Grade Characteristics (positive/negative) 4.4 Intelligent / Stupid 4.3 Diligent / Lazy 3.9 Humanistic / Not Humanistic 3.8 Responsible / Irresponsible 3.5 Precise / Sloppy 3.4 Influenced / Not Influenced by Peer Pressure 3.4 Imaginative / Not Imaginative 3.4 Open Minded / Closed Minded 3.3 Straight (“Like an Arrow”) / Not Straight 3.2 Calm / Anxious 3.1 Apprehensive / Not Apprehensive Regarding Others 3 Moral / Not Moral 2.9 Exciting / Boring The students exhibit a balanced image of the real scientist. The main quality that typifies the scientist is his or her high intelligence. The scientist is also diligent, responsible, and human. The participants found it difficult to decide, or did not know, whether scientists have a high level of morality and care for others, and whether they are exciting or boring by nature. Israeli student teachers marked the property curious as the scientist’s main virtue in Rubin’s 2003 study. SCIENCE AND IT’S IMAGES 151 Overall Image of the Scientist – Written Essays This section will present the images of the scientist which appeared in the free and the more focused versions of the essays (Appendix 2). The two main images of the scientist were positive and ambivalent. A non-evaluating descriptive image, where the participant describes the scientist, working habits, and characteristics also appeared. Positive Images of the Scientist One third of the learners gave a positive image of the scientist. They relied on a number of sub-categories. a) Cognitive abilities of the scientist. The positive image of the scientist were based mainly on cognitive abilities: high level of intelligence and wisdom (more so than the regular). Some participants also stated that the scientist is creative and has analytical abilities. In order to get results, it is the scientist’s responsibility to be more than someone with a high level of intelligence. He needs to have a conceptual vision and an extra ordinary way of thinking; he needs to be a genius, with sharp perception and the ability to pay attention to small details. I believe that the mental qualities, such as creativity and brilliance, make up the scientist. b) The social advantage of the scientific endeavor. The second main justification for the positive image attributed to the scientist was the social advantage that results from the scientific work and the responsibility that should accompany it: It is the scientist’s responsibility to save worlds. For example, when a scientist discovers a new medicine for a dangerous disease, or an ailment which has no cure, he really saves lives. c) Personal characteristics. Some participants mentioned moral responsibility and commitment: These people appear to be responsible individuals, who do not expose the public to experiments which may harm them. The image of the scientist is perceived in my view as a character. The scientist, in all his commitment, devotes his life to science, for good. 152 PAZIT KOREN and VARDA BAR Ambivalent Images of the Scientist The general image of the scientist as it appears in the written assays is ambivalent. He or she is perceived in various ways, ranging from giving, friendly and sociable, to having a strange personality. These conceptions are based on the contradiction they find between personality, the incentive for research and the social benefits of it, with a stigma of being lonely and strange, and morality that might be deficient. They relied on a number of sub-categories: a. The scientist has a high I.Q. but there the stigma of the mad and un-social scientist is problematic. The scientist is truly a genius. However, the more he succeeds in an experiment, even if I want to believe him, it would be difficult for me, because in my mind exists only the myth and the stigma of a crazy person. They seem like very smart, serious, responsible, and dedicated individuals who like challenges. But, I see them as being secluded from the rest of the population, either in the family setting or in the work setting, and also in the political world. b. The scientist has the ability to be useful to society and not damage the environment, but can also bring damage. He helps and advances society. With this in his mind, he can also discover things which are destructive and dangerous to society, like the atom bomb. c. The scientist’s work is important and motivating but it is also involved with material rewards and prestige. It always seemed to me that a successful scientist in his field feels great satisfaction. The prizes and grants which the scientist receives for his discoveries may be reason to arrogance. d) Subcategories that connects different categories. The scientist has an impressive personality which functions in a poisonous manner. The scientist has an impressive image of someone possessing intelligence, curiosity and analytical abilities beyond the normal. However, it seems like a too serious image, alone in the laboratory; it seems a bit frightening. His intelligence and breakthroughs can even lead to undesired results. The scientist benefits society but has anti-social behavior. SCIENCE AND IT’S IMAGES 153 On one hand, his actions are meant to better humanity. On the other hand, we must be skeptical about the scientist’s role as a man and not a researcher. In the end, he has no function as a man, to make a family, to bring usefulness to his close surroundings. The scientist advances technology but causes social harm. In a best case scenario, the scientist creates a chemical substance or something similar, and tries to discover its composition, previously unidentified. In a worst case scenario, mice are added to the setting of the room, to be used for experiments. A Non-evaluating Descriptive Image of the Scientist A number of participants described the domains of research relevant to the typical scientist, but did not evaluate this view. They believed that science deals only with the natural sciences: Chemistry, physics, and biology. In contrast, others stated that science deals with all fields of life including the humanistic sciences. Some emphasized the fact that the study is based on a clear scientific method; science is presented generally as experimental. The scientist brings up and examines assumptions, conducts experiments, sometimes succeeds and sometimes fails. As an example, the scientist deals with a question, to which he wants to find an answer, will conduct a study on it, interview people and compare the results. This perception is an indicative approach to science. “He, makes assumptions and examines them,” but it does not relate to the theoretical background on which these assumptions are made. Identification with Some of the Images Which Appeared in the Literature and the Moral Response to Them Group A: Identification with the ideological dilemma that confronted the scientist Walter Reed as presented in the book The Microbe Hunters (De Kruif, 1926). In this group, the participants were faced with the ethical dilemma which deals with the endangering of human lives in order to stop the spreading of a certain disease (the dilemma with which Reed deals). The percentage of participants who stated that the scientist acts (asking his friends to get infected by the yellow fever disease in order to determine the cause of the yellow epidemic) are justifiable was higher (42%) than the percentages of those who disagreed (36%). Fourteen 154 PAZIT KOREN and VARDA BAR percent of the participants of the sample were unsure whether Reed’s acts could be justified (the rest did not answer). Some of the participants who justify the scientist acts see the saving mankind as grand goals, in spite of endangering some individuals: “In order to save mankind we must harm the individual,” “the benefit of society delays the benefit of the individual.” Others stated that such an action is done for the advancement of science, which constitutes a goal of its own: “For science, we must sacrifice the victim, especially in extreme and dangerous situations,” “Scientists need to progress (and develop) their study any way.” Few participants had the opinion that the individual exposed to danger should agree to this exposure, in order to decrease the ethical responsibility of the experimenter: “If his friends agreed, then chances are that it was a serious disease and the need to find a cure was great.” “Sometimes we are skeptical to make changes and a push from friends can be beneficial.” Another claim stated that the goal sanctifies the resources: “He attempts to research something and he will ‘go all the way’ in order to find the answer to the experiment.” Others stated their opposition to this act saying that: “only if he is certain in that his assumptions are correct and can support it, he could do it.” A mixed approach towards supporting the lives of people results from the consideration of the individual in contrast to the consideration of the whole. The participants are of the opinion that on one hand, we must consider the welfare of society, and on the other hand, the harm caused to the lives of the participants in the experiment: “On one hand it is forbidden to play with the lives of others unless the goal and the way are clear.” “For the same reason that his friends agreed and knew that their lives were in danger, apparently everyone wants their name to be associated with scientific progress in order to prevent the death of many others.” Although from the beginning, “he did not have the consent of others, but they thought it might help them some how.” Others, with a moral consideration in mind, said that exposure to danger is dependant on the individual’s agreement. Even if he chooses to take this action, he must take all measures to avoid harm to others: “It depends whether he vaccinated his friends or gave them medicine,” “we must check all the other options and then we may ask if he made every effort to avoid the dangerous phenomenon.” Those who negate the endangering of individuals state that: “he only needed to endanger himself or to be satisfied with his hypothesis which was not proven.” Other participants negate conducting of any experiment on man on an ethical basis: “It is not ethical nor it is moral; SCIENCE AND IT’S IMAGES 155 it is forbidden to conduct experiments on humans, only on a group of cells.” “They should have tried it on animals.” Some considerations regarding the Jewish law (“harming the major principles of Jewish law”) and fear from the spreading of the problem (“multitudes becoming infected with the virus, if it escapes”) were observed especially amongst the religious participants. Group B: Identification with the scientist Frankenstein and his scientific work. This section of the study was conducted in the form of discussions, which allowed the existence of interaction amongst the interviewees. The novel Frankenstein has two different interpretations: the book stresses the responsibility of the scientist and his wrong doing while neglecting his creation. The movie on the other hand, stresses the fact that the scientist’s sin is in his hubris as playing God, and trying to create life. The participants were faced by the movie and influenced by its interpretation. This approach is strengthened by the fact that some of them were religious. Only a few students justified Frankenstein’s behavior. The most common explanation of the pupils to the scientist’s actions is his desire to face the problem of death: “He wants to resuscitate people so that they will not die.” “He said that we would be able to create body parts, from replacement parts.” The emotional (personal) motivation, which stems from the trauma he experienced when his mother died, was also mentioned: “So that he will be able to bring his mother back to life.” A few students mentioned his desire to advance medical science: “He is a very ambitious scientist. He tries to research the sciences which no one else wanted to research to progress science.” Others mentioned that we are to judge the act according to the objectives of the scientist to advance society: “His intentions are good [to aid society], but he became too excited to think about what the Professor told him that the result will be very distorted and this must not happen.” Most of them objected the idea that a human being can create life, especially because it goes against God’s laws and human reason: “He pretends that he can be the same as God and do what is meant not to be done, he thought that nothing will go wrong, and then in the end something does go wrong.” “He did not accept the fact that death is part of life,” “it is difficult to understand that someone is trying to do this, because it is going against nature.” The feeling that Frankenstein’s behavior in "on the border" of madness was also common: “He is crazy ... he has to resuscitate dead 156 PAZIT KOREN and VARDA BAR people, to attach all these things to them from other people’s body parts ... no sane person would do such a thing.” One participant stated that Frankenstein’s case was exceptional. “Most people, at least with the abilities of a scientist, would have come up with the idea that it is not a good idea to resuscitate anyone who has died. [Frankenstein] has the basic image of crazy, irresponsible scientist." Others thought the ethical limits were stretched in the movie: “It is unethical to create life because then it is a living thing that cannot be gotten rid of. In the end, it avenges him.” “It is already a living creature. He tries to kill it. It is difficult because of a moral viewpoint.” (A similar perspective exists in Crichton’s Jurassic Park). Several of them emphasized his lack of responsibility toward the creature he created: “His problem is that he sees the existing problem, but instead of fixing it he neglects it. Instead of dealing with it, he leaves; this is not responsible.” “It is like neglecting somebody who is seriously ill.” Many opinions regarding Frankenstein behavior were ambivalent: Although the motives of the scientist are good, the execution is not: “This work is ‘half-Frankensteinesque’: One has to make normal people who also look good. We cannot make half something,” “his intentions were relatively good to help the doctor and humanity afterwards he tried to fix it. But it was kind of too late.” He handles the method of the experiment, but it lacked consideration: “He had the tools and the scientific knowledge to make things, but he was much trapped: He did not foresee the destructive results,” “he did not doubt the outcomes of the research, or listen to others’ advice.” The situation lead to a split in his personality: “He has his normal life and the scientist in him which he does not control. He prefers to get cholera, but he has to make the discovery. This is excessive.” The stubbornness towards reaching the goal causes him not to look at the results: “The scientist looked through the Professor’s notebooks. He saw that a distorted man that had been created and he thought, ‘never mind.’ He wants to reach a goal and does not look at what is supposed to happen.” Discussion This study deals with contemporary learners attitudes toward science and the scientist, based on images that were depicted in some classical and popular literature that influenced our contemporary culture. The SCIENCE AND IT’S IMAGES 157 novelty of this research dwells in the testing of the values and social aspects that accompanied science in the past and in the present. This way of seeing science gives us the opportunity to see it as it was seen in the time when each break-through was achieved, in the public. Classical literature always followed the frontier of science and reflected the fear and anxiety towards each new finding. It was cosmology in the time of Galileo, electricity when the inventions of Franklin and Volta were made, chemistry towards the end of the 19th century, the atomic bomb in the middle of the 20th. Now it is cloning and misuse of drags found also in the views of our contemporary learners. The project was conducted among highly educated, literate, and motivated high school students. We assumed that these qualifications were needed to better cope with the targets of this research. Indeed, the results of the various tools used in the research: the questionnaire, the essays, and the interviews reflected deep thinking processes of the participants and an awareness to the complexity of the issues that were raised. The participants exposed a wide range of considerations and conflicts regarding their value judgments of science and scientists. The participants responded to the challenge of the conflict that they were faced with, and expressed their own conflicts regarding the problems stated. Contemporary students are sensitive to motives that express ambivalent and negative images of science as found in the literature, although they are separated by some 300 years from the time when some of the creations were written. Naturally, the way a great writer supports his ideas would be different than that of an average high school student. The attitude towards science as threatening was found in great writers as well as among contemporary students. The horror that was triggered by the strong passion towards science in the time of Dr. Faustus (1480-1540), due to the use of magic and witchcraft in order to achieve this knowledge, was replaced by the fear of the knowledge itself and mainly of the devastating results of keeping confidential knowledge and the technological uses of this knowledge. This issue was discussed in the play The Physicists (Durrenmatt, 1988), with the background of fear from dangerous technological developments such as atomic bombs. Our contemporary students’ attitudes towards knowledge are mostly ambivalent – they express admiration for science but it is mixed with expressions of criticism and fear. 158 PAZIT KOREN and VARDA BAR The image of the scientist as unsocial and emotionless reminds of Swift’s writings (1726/1998), who characterized the scientists as having such “high and sublime thoughts, that they cannot speak or hear whatever he tells them” (p. 160). The same idea was also expressed by an eleven- year old girl in Sagan’s book (1997), who described the scientist as a “nerd” and also in Song & Kim (1999), and finally in a description of a scientist by a participants of this study: On one hand, the act [of the scientist] is aimed to improve humanity, while on the other hand, one should be skeptical of the scientist’s function as a human being and not as a man of research, because eventually a human being should function as a human being. These views still exist in spite of the fact that science is regarded as practical against the view of Swift. While Swift in 1726 felt that science was not effective in regulating our lives, students regard science as useful to the society. Students connect admiration of science with fear from it, criticize the scientist as a nerd, and also add moral judgment to the achievements, and the way they were accomplished. Moral judgment was found in views expressed by the pupils that observed the movie Frankenstein, though some justified the scientist as contributing to the progress of science in general and medicine in particular, or as being motivated by a personal acute trauma. Most of them opposed his act and said that it is against the ethics of science, by leading to irreversible results, because one cannot kill a living creature after creating it, even if it is damaged. Others pointed out, that both the experiment and the scientist are illogical, and on the border of madness. Others criticized his irresponsibility towards the outcomes of his creation: “instead of taking care of him, he neglects him. Instead of coping with it, he quits.” It is interesting to mention that the participants do not attribute danger to electricity, they already know that the experiment in Mary Shelley’s book is wrong, most of them resented the idea of cloning which they were opposed to when asked directly by saying that it is “wrong to create life to play God.” Criticism on the grounds of moral reasons was also found in relation to the protagonist of the story in the Microbe Hunters, the scientist Reed, who examined the yellow fever virus. While some participants identify with the rhetoric of the author of the book, who justifies the scientific research and even refers to it with admiration, many participants criticized Reed. Varied justifications were given – it is not justifiable to do experiments with human beings, the subjects did not SCIENCE AND IT’S IMAGES 159 consent, the experiment might go wrong, and the participants and the rest of the population will be endangered. It may be that there are other ways (more moral ways) to do the experiment. Participants also asked whether all options were examined, and what is the scientist’s level of confidence in his theory. Various outcomes in relation to the image of the scientist were emphasized in the findings of the closed questionnaire (the agreement with the personality characteristics of the scientist). The list of qualities that were attributed to scientists as being intellectual, diligent, methodic, logical, and dedicated to his work. This image was also found in two popular science books written by two scientists, Smoot (1995) and Shapley (1967), which summarized many years of work. Nevertheless, less positive characteristics, such as being influenced by peer pressure were offered. Participants were also indecisive regarding to the level of morality and the ability to care for others regarding the scientist. One of the most significant findings in the present study is the participants’ serious doubt in regards to the viewpoint of the different perceptions which were tested. The findings of different tools which were used in this study point to the complicated image of the scientist. In contrast to the popular research image of the scientist presenting him as an anti-social, lonely researcher who works in a laboratory and uses equipment suitable for a school laboratory, like bottles and test tubes, in this study’s participants presented a mixed image of the scientist. A few of them stated that the scientist is isolated from any social framework, from the family web, and interaction in the political world. Others stated that the scientist is connected to society, possessing good communication skills and is open to others’ views. In addition to the characteristics that refer to the scientist’s intelligence – stubbornness, hardworking – the scientist was presented as enthusiastic, open to the beauty of nature, and some possessing excellent social talents. They describe scientists that can move large groups, to convince people of different fields, to help them and to raise money for the execution of research projects. Scientific and Applicable Conclusions This is a unique study about evaluating the attitudes of students in Israel towards science and scientists. We recommend that an additional study be conducted among other population groups that were not investigated here, in other words, with low literacy groups. This may be done by adapting the research tools to fit those population groups. 160 PAZIT KOREN and VARDA BAR Practical usefulness was not the main consideration of this study. Its contribution lies mainly in the dimension of insight it provided on the relationship between humanism and science as appeared in the literature and in the views of contemporary learners. Several suggestions are provided. We suspect that negative attitudes towards science and scientist sometimes stem from ignorance. As a result we recommend that students be equipped with enough knowledge to enable them to formulate a considered opinion. We suggest exposing students to science-related works, similar to those analyzed in the theoretical background. Exposure to popular science books, written by the researchers themselves, will introduce the students to real scientists and their work methods. It will also increase their motivation (Smoot, 1995). The collective interview method was found to be particularly suited to the special population that took part in this research. We recommend that this method be adopted for instructing populations with similar characteristics. It should also be adopted for value related issues connected to science and scientists, and for other ethical subjects. Appendix 1 Essays on the Perceptions of the Scientist’s Image Form A: Free Writing – Write an essay on the image of the scientist as you see him or her. Form B: Structured Writing – Write an essay on the following topic: In society there exist different images in relation to the scientist. Some see him or her as a crazy person, dangerous or irresponsible, and some emphasize positive qualities in the scientist’s personality and in his or her work for the benefit of society and the advancement of science. Express your opinions on the image of the scientist as you see it, using the described two images. Appendix 2 Semi-structured Group Interviews Group A The book The Microbe Hunters tells about a scientist named Walter Reed who researched the possibility of preventing the yellow fever virus, which caused a plague that killed thousands of American soldiers more than one hundred years ago. In order to prove that mosquitoes carried the virus, and because animals couldn’t become infected with the virus, Reed had no other choice but to ask his three friends who participated in his study, to get stung by mosquitoes carrying the virus and by doing so, endangering their SCIENCE AND IT’S IMAGES 161 lives. In you opinion, did Reed act properly? How would you act in the same situation? Group B The movie which you have just watched, tells about a scientist (Frankenstein) drunk with success, who ignores the warnings of others and creates a living creature from body parts. The monster he created accuses him of sin and finally turns against its maker. 1. Please judge the scientific action in the work. Refer to his idea to create life from nothing. What were his motives for doing such? How would you describe the idea, positive or negative? Did he use logic? Please refer to the question of responsibility towards the creature which was created (monster). 2. Please express your opinion about the scientist himself (Frankenstein). What type of scientist is he? What are his characteristics? What is the image of the scientist which stems from the movie?	Translation - Serbian Nauka i njena lica-Obećanje i Pretnja: Od klasične literature do predstava o nauci savremenih studenata i „Naučnik“. PAZIT KOREN VARDA BAR Jevrejski univerzitet u Jerusalimu Odlomak: Proučavani su fizički i socijalni doživljaji naučnika među školskom decom, profesorima i učiteljima tokom poslednjih 50 godina. Interes je pokazan i za percepciju ličnosti iza fizičkog stereotipa. Kako god, vrednosne procene nauke i naučnika, i pozitivna i negativna predubeđenja vezana za te procene u našem društvu, manje su istraživana činjenica, kako u Izraelu tako i drugde. Istraživani su modeli dodeljeni nauci i naučnicima u klasičnoj literaturi, kao i od strane određenih pisaca popularne nauke, zajedno sa viđenjima današnjih učenika. Populacija se sastoji od 125 srednjoškolaca iz Izraela. Nekoliko alata je korišteno za dešifrovanje njihovih predstava o nauci: upitnik, pisanje eseja i delimičnostruktuirani grupni intervjui. Klasični autori imaju pesimistične poglede na nauku i naučnike. Većina modela su nefavorizujući i kritikuju naučnike: Ludi i monstruozni naučnici( Frankenštajn), naučnik odsečen od realnosti, čudak ( Guliverova Putovanja), naučnik čija neodgovorna istraživanja ugrožavaju okolinu( Park iz doba Jure) i naučnik čija glad za znanjem ne pita za cenu( Faust). Pozitivne predstave nalaze se u literaturi popularne nauke: naučnik koji leči bolesti ( Lovci na mikrobe), naučnik koji ima profesionalni integritet( Galaksije), naučnik koji se pridržava pravila naučnih metoda u cilju ostvarenja objektivnih rezultata ( Zarezi u vremenu). Otkrili smo da se neki izrazi vezani za strah od nauke, koji su se javljali u klasicima od početka 18. veka, mogu pronaći u sličnom obliku i kod studenata u 21. veku, dok su neki pokazivali isti strah ali na različite načine. Postojala je takođe identifikacija sa Sviftovim „asocijalnim i neemotivnim“ naučnicima. U korak sa postojanjem straha od nauke i uglavnom ambivalentnog misljenja učenika o nauci, dominantna slika koja proizilazi iz ove studije je ta da su današnji učenici pronaučno nastrojeni i doživljavaju nauku kao korisnu sferu društva. Kljucne reči: Predstave o naučnicima, modeli nauke i naučnika, naučni koncepti, stavovi prema nauci, srednjoškolci, Izrael, klasična literatura, popularne knjige, etika, moralni sud, dilema. UVOD Uticaj nauke na ekonomski, socijalni i tehnološki napredak je fait accompli . Mnoge predstave ponašaju se u skladu sa ovim: osećaji, evaluacije pa čak i želje i fantazije. Neke od njih su pozitivne: nauka iskazuje duh progresa, razvija ljudski duh, unapređuje i produžava naše živote, dok drugi izražavaju sumnju i strah, sluteći da naučni kapaciteti seju razaranje i katastrofe. Ove emotivno nabijene vizije koje prate nauku, a naročito one kritičke podstruje odnosa između nauke i društva, nisu uvek otvoreno izražene u društvenim i javnim konverzacijama, ali često nalaze svoj izraz u umetnosti, naročito u književnosti, pozorištu i filmu: Klasična kreacija nije ogledalo ni tačan odraz onog što je poznato i razumljivo svima: ona dozvoljava da se iskažu strahovi koji se inače ne iskazuju otvoreno, ako se uopšte i iskazuju, a sve zbog mogućnosti da se iskušavaju tabui i društveno prihvatljivi moralni sudovi. ( Haynes, 1994, p.313) Vrednosti primenjivane na nauku i naučnike koji se pojavljuju u klasičnoj književnosti, vrlo retko su ispitivani od strane današnjih učenika-neke od ovih predstava su se tokom vremena razvile u društvene mitove i simbole ostavljajući svoj trag na naš stav prema nauci. Iz tog razloga je ova studija važna. Tokom poslednjih 50 godina, mnoge studije su sprovedene među školskom decom, nastavnicima i profesorima, glede njihove percepcije profesionalnog i socijalnog imidža naučnika. Inovativnost ove studije je njeno detaljno ispitivanje stava studenata prema nauci i naučnicima, drugim rečima njihovog vrednovanja nauke i naučnika i pozitivnih i negativnih stavova vezanih za ova vrednovanja. Da bismo ovom radu dali širu kulturnu referencu, kao osnovu studentske kritičke evaluacije uzeli smo nekoliko dela popularne nauke i klasične literature obuhvatajući period od početka 18.veka do druge polovine 20.veka. Ocenjivali smo da li studentske predstave o nauci i naučnicima pronađene u istraživanju, kao i emotivni odnosi koji ih prate, odražavaju mišljenja klasičnih autora tokom istorije, i da li je moguće pronaći u mišljenjima studenata, paralelno sa pozitivnim etosom i kritičku evaluaciju ili sumnju prema nauci. Koliko je nama poznato, detaljna studija uz upotrbu primera iz klasične i popularne literature, kao što je slučaj sa ovom, još uvek nije sprovedena, kako u Izraelu, tako i u inostranstvu. Ova studija ima sledeće ciljeve: 1. Da prikaže studentske predstave o naučnicima iz dve perspektive: tipične karakteristike naučnika i ukupni imidž nje ili njega. 2. Da ispita stepen do kog su se studenti identifikovali sa modelima nauke i naučnika koji se pojavljiju kod pojedinih autora popularne nauke i klasične literature. 3. Da ispita da li postoje sličnosti između vrednosti pripisanih nauci i naučnicima od strane današnjih studenata i onih predstavljenih u klasičnoj literaturi( vidi diskusiju). TEORETSKA POLEĐINA Razlike koje postoje između naučnih koncepta i stavova prema nauci Termin naučni koncepti odnose se na koncepcije i miskoncepcije koje deca različitog uzrasta imaju prema naučnim domenima ( Nussbaum&Novick, 1982), kao što je koncept živih bića npr( Koren & Bar, 2007). Koncept je mentalni model objekata, osećaja i emocija, onako kako se isti reflektuju u mozgu svakog pojedinca ponaosob. Ova slika funkcioniše kao osnova organizovanog, prilično razumnog pogleda na svet oko nas. Rezultati istraživanja su pokazali da deca ne ostvaruju formalno obrazovanje kroz tabula rasa većkroz prethodna zapažanja. Studije pokazuju da deca( pre svog formalnog obrazovanja) nisu uvek saglasna sakonvencionalnim naučničkim konceptima. Njihove koncepti se postepeno menjaju i približavaju prihvaćenim naučnim koncepcijama. Učenje naučnih koncepta i koncepcija je proces konceptualne promene gde učenik svoje prethodne koncepcije zamenjuje naučno prihvatljivim. Termin stavovi prema nauci odnosi se na spoljne sudove o nauci i naučnicima sa tačke gledišta etike, estetike, emocija koje pobuđuju, njihove korisnosti i štetnosti i njihovog uticaja na društvo, okolinu i porodicu. Stav prema nauci može biti deo bilo kog vrednosnog okvira koji prihvata ili poriče validnost nauke. Ispitivanje stavova prema nauci pokazuje da se o naučnim teorijama i predstavama ne sudi samo na osnovu njuhove logičke verifikacije, već takođe i prema uticaju koji imaju na društvo i pojedinca. Stoga, sud o nauci je kombinacija logike( činjenice, pravila, teorije), vrednosti ( verovanja) i osećanja( motivacija), koji zajedno čine nerazdvojne delove vrednosnog suda pojedinca. Određen broj istraživača stoji iza činjenice da naučno obrazovanje ne priprema decu za odgovorno funkcionisanje u društvu koje je pod uticajem nauke i tehnologije. Stoga, na obrazovnim programima je da potpomognu usvajanje naučnih znanja i razvoj veština, vrednosti i mišljenja.( Američka asocijacija za napredak nauke, 1993). Tekuće istraživanje evaluira vrednosne sudove o naučnicima, tj njihov odraz u literaturi o među današnjim đacima. Klasični radovi su uzeti kao osnova evaluacije. Prema Putnamovom mišljenju( 2002), nikada nije postojala jasna dihotomija između objektivnosti( naučni koncepti i percepcija nauke) i subjektivnosti (stav prema nauci), i to je problematično, pošto su mnogi opisi ljudskih poduhvata u isto vreme i činjenični i dati u obliku ličnog suda. Prethodne studije koje su se bavile predstavama o nauci i naučnicima Fizički i Socijalni imidž naučnika Mead and Metraux (1957) su otkrili da je popularna percepcija naučnika među srednjoškolcima belac sa bradom, naočarima i laboratorijskim mantilom koji radi za laboratorijskim stolom koristeći boce, epruvete i hemikalije. Ova slika bila je prisutna u zapadnom svetu, bez obzira na zemlju i uzrast ( Song&Kim, 1999; Spitulnik, Stratford, Krajcik&Soloway, 1998). Ova fizička slika naučnika takođe se javljala i kod profesora u Nigeriji( Mbajiorgu&Iloputaife, 2001) i naučnim predavačima u Indiji( Rampal, 1992). Pokazan je takođe interes i za percepciju ličnosti koja stoji iza fizičkog stereotipa. Slika koja postoji o naučnicima među studentima je ona o ljudima koji su vođeni željom da šire blagodeti znanja i objašnjavaju svet oko sebe.( Beardslee&O’Dowd 1961). Rampal (1992) otkrio je da su naučni predavači u Indiji videli naučnike kao osobe koje poseduju izuzetnu sposobnost i snagu karaktera i koje su veoma posvećene traganju za istinom. Naučnici su odavali utisak veoma zaposlenih ljudi koji su većinu vremena igledali izgubljeno. Karakterisani su „ naučničkim temperamentom“ koji se odražavao kroz sve aspekte njihovog života. Prema našim najnovijim istraživanjima, naučnici su intelektualci, temeljni, metodični i logični ( Koren&Bar, u štampi), slika koju možemo naći i kod Lederman, Wade i Bell( 1998). U svojoj knjizi Demonom progonjeni svet: Nauka kao sveća u mraku, Sagan(1995) opisuje još jedan stereotipski opis naučnika: Jedno od stereotipskih zanimanja je nauka. Nučnici su štreberi, društveno nesposobni, rade na nerazumljivim temama koje nisu zanimljive nijednoj normalnoj osobi, čak i da ista ima volje da svoje vreme troši na taj način, što opet nijedna razumna osoba ne bi činila. „Živite!“, prosto poželite da im kažete.(st.362). Skorija istraživanja naglašavaju uticaj koji kulturni milje ima na predstavu o naučniku ( Finson, 2003; Fung, 2002; Stefansson, 2006; itd.). Sjoberg,(2002) je otkrio ( kod 20 država) razliku između predstave o naučnicima kod đaka starih između 12 i 13 godina u razvijenim i nerazvijenim zemljama. U industrijalizovanim(razvijenim) zemljama, predstava je stereotipna sa određenim, ne previsokim, procentom „ludih naučnika“.. U Izraelu, ovakva predstava se može sresti samo kod đaka mlađih od 11 godina. Neki đaci u Zapadnim zemljama eksplicitno kažu da naučnici žele da pomognu drugim ljudima i da u tome i uspevaju. U nerazvijenim zemljama, naučnici se doživljavaju kao heroji, hrabri, inteligentni, pomažu drugima, leče bolesne i svima poboljšavaju uslove života. Andersonovo istraživanje među đacima i Gani(2006) pokazalo je da se naučnici doživljavaju kao humanitarni radnici, neko ko pomaže siromašnima i obespravljenima, heroji društva. Ovi aspekti se retko spominju od strane đaka na Zapadu ( Anderson, 2006; Sjoberg, 2002.) Populacija koja je učestvovala u ovim istraživanjima može se klasifikovati kao Zapadna i ona iz razvijenih sredina. Kao što je navedeno u ovom pregledu, većina projekata koji su spovedeni u vezi sa predstavama o naucnicima ispitivala je njen ili njegov fizički i socijalni imidž. U određenom broju studija, studenti su ocenjivali karakteristike naučnika, ali bez komentarisanja istih. Detaljno ispitivanje emotivnog stava prema nauci i naučnicima kao pretnji, što ima uticaj na studentsko poverenje u progres, i poređenje takvog stava s onim nađenim u literaturi retko je sprovođeno. Stoga se veruje da je ova studija unikatna u tom pogledu. MODELI NAUKE I NAUČNIKA U POPULARNOJ NAUČNOJ I KLASIČNOJ LITERATURI Iako je u našoj kulturi pozitivni etos o naučnicima široko rasprostranjen, izrazi straha od nauke i kritika njenih osobina i uticaja koji ima na ljudske živote čine podstruju u odnosima prema nauci. Campbell( 2003) tvrdi da je osećaj straha od nauke delimično rezultat nedostatka dovoljne naučne pismenosti. Kako god, on je primarno rezultat individualnih pogleda na svet uopšte, a posebno na nauku. Ovaj deo rada predstavlja pregled multivrednovanih izjava u odnosu na predstave o nauci u klasičnim radovima koji su napisani od 18. do druge polovine 20. veka. Pošto većina ovih radova kritikuje nauku i odražava strah od njenih posledica, izabrali smo da ih ublažimo analizirajući većinom pozitivne predstave o nauci i naučnicima koje se javljaju u knjigama o popularnoj nauci. U okviru ovog rada koncentrišemo se samo na književna dela, koja su čvrsto uticala na percepcije u našoj kulturi. Pozitivne predstave o nauci nađene u nekim delima popularne nauke a) Naucnici koji leče bolesti. Walter Reed, Anthony van Leeuwenhoek, Louis Pasteur i Robert Koch su predstavljeni kao „ dobrovoljci na usluzi svetu i promoteri nauke“. Njihov zadatak je bio da se bore sa bakterijama, „uzrocima strašnih epidemija“. Kako bi odredio uzrok epidemije žute groznice, Reed se našao pred etičkom dilemom da li da ugrozi živote drugih(svojih kolega) i učinio je to za dobrobit čovečanstva( kao što je navedeno u Lovcima na mikrobe, De Kruif, 1926, Uvod) b) Naučnik koji ima profesionalni integritet Sharpley, kao što se može videti u njegovoj knjizi Galaksije( 1967) nije zainteresovan za prestiž, birajući čistu naučnu istinu nauštrb svog ličnog mišljenja. Njegova veličina leži u prihvatanju superiorne teorije njegovog profesionalnog kolege (Hubble) u pogledu strukture galaksija, njegovo propagiranje dve naučnice( Henrietta Lewitt i Annie Cannon) čija otkrića su poricala njegovu teoriju, i njegove radosti kada je naučna slika otkrivena. c) Naučnik koji se pridržava pravila naučnih metoda u ostvarivanju cilja i tačnih rezultata. Nauka, po Sharpley ( Galaksije, 1967) i Smoot (Bore u vremenu, 1995) zahteva naporan i tačan rad. Posebnu pažnju bi trebalo obratiti na zaštitu od ishitrenih zaključaka, a instrumenti moraju biti često kalibrisani zarad pouzdanih rezultata. Uprkos velikoj odgovornosti, naučnici smatraju da je ovo interesantno i prijatno. „ Skretanja sa puta( novi instrumenti koji se moraju isplanirati i izraditi ) mogu sami po sebi biti interesantni“. (strana 1). Predstave o naučnicima u klasičnim radovima koji kritikuju nauku i naučnike a) Ludi i monstruozni naučnik. Slika ludog naučnika, kao Dr. Jekyll i Mr. Hyde ( Stevenson, 1886/2001) i drugi, bila je čvrsto posađena u javnu svest stvaranjem Frankenštajna(Shelley, 1818), naučnika sa neobuzdanom ambicijom koji stvara živo biće od delova tuđih tela. Oopijen uspehom, ignoriše upozorenja drugih i umišlja da je bog. Monstrum kog je stvorio opužuje ga za greh nemara i naposletku okreće protiv svog tvorca. b) Naučnik otuđen od ljudskog života. Naučnik čije je iskustvo o stvarnosti svedeno na njegove analitičke sposobnosti prikazan je u drami Fizičar (Durrenmatt, 1988). Glavni junaci su trojica naučnika koji hladnokrvno i proračunato ubijaju sestre u ludnici, tvrdeći, kao optuženi u Nirnberškom procesu, da “naređenje je naređenje“. c) Naučnik odsečen od realnosti-„čudak“. U svojoj čuvenoj knjizi, Guliverova putovanja, Jonathan Swift (1867/1998) istupa protiv Kraljevskog društva naučnika Engleske, koji su predstavljeni kao“ različiti po ponašanju od drugih ljudi,“(st. 160), upuštajući se u blesave aktivnosti i sa nedostaatkom socijalnih veština. Preokupirani su „ uzdignutim i uzvišenim mislima, u toj meri da ne umeju da slušaju druge niti da razgovaraju sa njima“, (st. 160). d) Naučnik čije je neodgovorno istraživanje štetno za okolinu. Iluzija da onaj koji koristi tehnologiju ima ekskluzivnu kontrolu nad prirodom otima se kontroli i prelazi u haos. Na ovaj način su prikazani naučnici u Jurassic Park (Crihton, 1990; Spielberg, 1993). Optužen je da ne može da shvati opasnosti koje nose njegova buduća otkrića i mogućnost kontrolisanja životne sredine. e) Naučnik žedan znanja po svaku cenu. Geteov čuveno delo ( Faust, 1918/1995) prikazuje ambivalentnu predstavu o naučniku. S jedne strane, intelektualna znatiželja ga vodi u istraživanje granica ljudskog znanja i čežnju za otkrivanjem moći koju ono nosi, ali s druge strane, on je glup i arogantan. Da bi ostvario svoj cilj, pravi ekstremno lošu nagodbu sa Đavolom koja uključuje čarobnjaštvo i jeftine trikove. Ove pozitivne i negativne predstave o nauci i naučnicima čine osnovu empirijske studije. Neke od njih su iskorištene i kao njen test. Empirijska studija Grupa Grupa se sastojala od 125 srednjoškolaca iz Izraela ( od devetog do dvanaestog razreda) oba pola iz različitih verskih i sekularnih grupa. Ova grupa srednjoškolaca došla je iz škola poznatih po đacima sa visokim prosečnim ocenama i motivacijom za učenje. Đaci dolaze iz napredne socioekonomske sredine i deca su visoko obrazovanih roditelja. Odluka da se fokus stavi na ovu grupu potiče iz očiglednih razlika u nivou motivacije i rezultata, koja je dobijena u preliminarnoj fazi ovog rada (nije prikazana ovde), a koja favorizuje grupu sa visokim nivoom pismenosti. Učesnici su gotovo ravnomerno podeljeni na oba pola, religiozni i nereligiozni, sa vrlo sličnim konačnim rezultatima, bez obzira na pripadnost. Malobrojni slučajevi razlika su ipak spomenuti jer i najmanje razlike zaslužuju raspravu. Alati i njihova primena Prvo, sproveden je upitnik sa ponuđenim odgovorima, tražeći od učesnika da moral i ostale osobine koje karakterišu naučnika. Upitnik svoju validnost bazira na istraživanju Songa i Kima iz 1999. Neke od karakteristika koje se pojavljuju u upitniku ( kao što je sud o moralnosti) dodate su u ovo istraživanje. Drugo, korišten je pisani esej o predstavama o naučnicima. Ovaj alat je predstavljen u dve forme: bez uputstava, i više fokusiran, tražeći od studenata da obrate pažnju na dve suprotstavljene predstave o naučnicima ( Dodatak 1.). Instrukcije za pisanje su date prilikom prezentovanja ovog zadatka. Treće, polustruktuirani grupni intervjui urađeni su sa različitim grupama srednjoškolaca ( svaka sa po 25 đaka). Ispitivali smo njihove stavove prema predstavama o nauci i naučnicima prikazanim u dva tipa dela, popularne nauke i klasične literature, kao i da ocene naučničke akcije sa aspekta morala. Grupa A se suočila sa etičkom dilemom Waltera Reeda spomenutom u Lovcima na mikrobe ( De Kruif, 1926) da li ugroziti živote drugih( među njima i svojih kolega) ne bi li otkrio uzrok epidemije žute groznice. Grupa B je intervjuisana nakon gledanja Frankenštajna( Dodatak 2). Glavna pitanja su korištena u polustruktuiranom grupnom intervjuu. Uprkos ovome, većina odgovora zabeleženih u intervjuu proistekla su iz slobodne debate učesnika koji su izneli različita mišljenja. Analiza podataka Studija kombinuje kvalitativni i kvantitativni pristup. Glavni cilj studije je analiza materijala iz ugla učesnika. Numerička analiza studentskih pogleda urađena je uzevši u obzir tipične karakteristike naučnika koje se pojavljuju u upitniku (istraživački alat broj 1). Informacije dobijene u esejima i intervjuima (istraživački alati 2 i 3) kvalitativno su analizirane oko centralnih i subcentralnih kategorija. Prezentacija subcentralnih kategorija praćena je direktnim reakcijama učesnika. Nazivi kategorija uzeti su iz terminologije istrazivača (primarna kategorija: evaluacija naučnika i naučnih metoda u stvaranju. Subkategorije: pozitivna evaluacija, negativna evaluacija, ambivalentna evaluacija). Nalazi Karakteristike(zasnovane na vrednostima) i druge( o naučnicima) u upitniku sa unapred datim odgovorima. Tabela 1 prikazuje prosečnu numeričku ocenu, od 1 do 5, svake karakteristike naučnika dobijene od strane učesnika. Ocena 5 pokazuje potpuno slaganje sa datom pozitivnom ocenom. Ocena 1 pokazuje potpuno slaganje sa suprotnom negativnom karakteristikom ili nedostatak znanja u vezu sa datom karakteristikom. Tabela 1. Karakteristike naučnika-numerička ocena Ocena Karakteristike (pozitivne i negativne) 4.4 Inteligentan/ Glup 4.3 Vredan/ Lenj 3.9 Čovečan/ Nečovečan 3.8 Odgovoran/ Neodgovoran 3.5 Precizan/ Aljkav 3.4 Pod uticajem/ Nije pod uticajem vršnjaka 3.4 Maštovit/ Nemaštovit 3.4 Slobodouman/ Ograničen 3.3 Ispravan/ Problematičan 3.2 Miran/ Anksiozan 3.1 Zabrinut za druge/ Nezainteresovan za druge 3 Moralan/Amoralan 2.9 Uzbudljiv/Dosadan Studenti pokazuju ujednačenu predstavu o stvarnim naučnicima. Najvažniji kvalitet koji karakteriše naučnika je njena ili njegova inteligencija.Naučnik je ujedno i vredan, odgovoran i human. Učesnicima je bilo teško da odluče, ili nisu znali, da li naučnici imaju visok nivo moralnosti i brige o drugima, niti da li su uzbudljivi ili dosadni po prirodi. Izraelski profesori označili su karakteristiku radoznalosti kao nalvažniju vrlinu naučnika u Rubinovoj studiji iz 2003. Opšta predstava o naučniku-pisani eseji Ovaj deo će prikazati predstave o naučnicima koje su se javljale u slobodnim i zadatim verzijama eseja (dodatak 2). Dve glavne predstave bile su pozitivne i ambivalentne. Neocenjujuća deskriptivna predstava u kojoj učesnici opisuju naučnika , njegove radne navike i osobine, takođe je bila prisutna. Pozitivne predstave o naučniku Trećina učenika ima pozitivnu predstavu o naučnicima. Oslanjali su se na određen broj subkategorija. a) kognitivne sposobnosti naučnika. Pozitivna predstava o naučniku uglavnom je zasnovana na kognitivnim sposobnostima: visok nivo inteligencije i mudrosti( viši od uobičajenog). Neki od učesnika su istakli kreativne i analitičke sposobnosti naučnika. Da bi dobili rezultate, naučnikova je odgovornost da bude više od nekog sa visokim nivoom inteligencije. Mora imati konceptualnu viziju i neuobičajen način razmišljanja; mora biti genije, oštre percepcije sa sposobnošću obraćanja pažnje na najsitnije detalje. Ja verujem da mentalne kvaliteti kao što su kreativnost i brilijantnost čine naučnika. b) Društveni napredak koji donosi naučni poduhvat. Drugo najvažnije opravdanje za pozitivnu predstavu o naučnicima je društveni napredak kao rezultat naučnog rada i odgovornost koja bi trebala da ga prati. Obaveza je naučnika da spasavaju svetove. Na primer, kada naučnik pronađe novi lek za opasnu bolest ili lek za do tada neizlečivu, on stvarno spasava živote. c) Lične osobine. Neki učesnici su spomenuli moralnu odgovornost i posvećenost: Ovi ljudi izgledaju kao odgovorni pojedinci koji ne izlažu javnost eksperimentima koji im mogu nauditi. Predstava o naučniku se , po mom mišljenju, doživljava kao karakter. Naučnik, sa svim avojim požrtvovanjem, posvećuje svoj život nauci, dobrom. Ambivalentne predstave o naučniku. Opšta predstava o naučniku, po pisanim esejima, je ambivalentna. Ona ili on se doživljavaju na različite načine, u rasponu od darežljivog, prijateljskog i društvenog do onog sa čudnom osobenošću. Ove koncepcije se baziraju na kontradikcijama između doživljaja nečije ličnosti, impulsa za istraživanje i društvene koristi od njega, praćenog stigmom usamljenika i čudaka sa nedostatkom moralnosti. Ove predstave se oslanjaju na više subkategorija. a) Naučnik ima visok IQ, ali postoji problematična stigma njegovog ludila i nesocijalne prirode. Naučnik je pravi genije. Ipak, koliko god da je uspešan u svom eksperimentu, čak i da želim da mu verujem, to bi bilo teško za mene jer u mojoj glavi postoji samo mit i stigma o ludoj osobi. Odaju utisak veoma pametnih, odgovornih i posvećenih pojedinaca koji vole izazove. Ali, vidim ih kao odsečene od ostatka populacije, bilo u porodici ili na poslu, kao i u političkom svetu. b) Naučnik ima sposobnost da bude koristan za društvo i životnu sredinu, ali može i da nanese štetu. Pomaže i unapređuje društvo. Sa ovim na umu, on takođe može izumeti destruktivne i za društvo opasne stvari, kao što je atomska bomba na primer. c) Rad naučnika je važan i motivišući, ali takođe uključuje prestiž i materijalne nagrade. Uvek sam imao utisak da uspešan naučnik oseća veliku satisfakciju. Nagrade i stipendije koje dobija za svoje uspehe mogu biti uzrok arogancije. d) Subkategorije koje povezuju različite kategorije. Naučnik poseduje impresivnu ličnost koja funkcioniše na otrovan način. Naučnik odaje impresivnu sliku nekog ko poseduje natprosečnu inteligenciju, radoznalost i analitičke sposobnosti. Ipak, odaje utisak preozbiljne osobe, usamljene u laboratoriji; izgleda pomalo zastrašujuće. Njegova inteligencija i probojnost mogu dovesti i do neželjenih rezultata. Naučnik unapređuje društvo ali ima antisocijalno ponašanje. S jedne strane, njegova dela su usmerena ka unapređenju društva. S druge strane, moramo biti skeptični prema naučnikovoj ulozi kao čoveka, ne kao istraživača, u stvaranju porodice, u korisnosti prema njegovom bliskom okruženju. Naučnik unapređuje tehnologiju ali prouzrokuje društvenu štetu. U najboljem slučaju, naučnik pronalazi hemijsku supstancu ili nešto slično i pokušava da otkrije njen ranije neidentifikovan sastav. U najgorem slučaju, miševi su deo nameštaja, da bi se kasnije iskoristili u eksperimentu. Neevaluirajuća opisna predstava o naučniku. Određeni broj učesnika opisao je domene istraživanja vezan za tipičnog naučnika, ali nije ocenilo ovu tačku gledišta. Verovali su da se nauka bavi samo prirodnim naukama: hemijom, fizikom i biologijom. Kao kontrast, ostali su izjavili da se nauka bavi svim oblastima života, uključujući i humanističke nauke. Neki su naglasili činjenicu da je studija bazirana na jasnom naučnom metodu; nauka je uopšteno predstavljena kao eksperimentalna. Naučnik iznosi i ispituje pretpostavke, sprovodi eskperimente, nekad uspeva, nekad ne, Na primer, naučnik se bavi određenim pitanjem, na koje traži odgovor, izvodi studiju o njemu, intervjuiše ljude i poredi rezultate. Ova percepcija je indikativan pristup nauci. „ On pravi pretpostavke i ispituje ih,“ ali ih ne dovodi u vezu sa teorijskom pozadinom na kojima su napravljene. Identifikacija sa nekim predstavama koje se javljaju u književnosti i moralni odgovor na njih Grupa A: Identifikacija sa ideološkom dilemom sa kojom se susreo Walter Reed, kao što je predstavljeno u knjizi Lovci na mikrobe ( De Kruif, 1926). U ovoj grupi, učesnici su se susreli sa etičkom dilemom ugrožavanja ljudskih života u cilju sprečavanja širenja određene bolesti ( dilema sa kojom se sreće Reed). Procenat učesnika koji je izjavio da je naučnikov postupak opravdan ( on pita prijatelje da li može da ih zarazi žutom groznicom ne bi li otkrio uzrok epidemije) je veći( 42%) od procenta onih koji se sa tim ne slažu( 36%). Četrnaest procenata učesnika nisu bili sigurni da li se Reedov postupak može opravdati. Ostali nisu ništa odgovorili. Neki od učesnika koji su pravdali naučnikov postupak vide spasavanje čovečanstva kao veliki cilj uprkos ugrožavanju pojedinaca: „ Da bismo spasili čovečanstvo moramo ugroziti pojedince,“ „ dobrobit čovečanstva odlaže dobrobit pojedinca“. Drugi su izjavili da se takva akcija sprovodi radi napretka nauke, što je cilj sam za sebe: „ Za nauku moramo nekog žrtvovati, naročito u ekstremnim i opasnim situacijama“, „ Naučnici moraju razvijati svoje studije u svakom slučaju“. Nekoliko uesnika je imalo mišljenje da bi individualac trebao sam da pristane na izlaganje opasnosti, da bi umanjio etičku odgovornost naučnika koji sprovodi eksperiment: „Ako se njegov prijatelj složio, to znači da je u pitanju ozbiljna bolest i da je potreba za pronalaskom leka velika“. „ Ponekad smo skeptični kad su u pitanju promene i podstrek od strane prijatelja može da pomogne“. Još jedan stav je da cilj opravdava sredstvo: „ On pokušava da istraži nešto i spreman je da ide „ do kraja“ ne bi li pronašao odgovor na pitanje“. Drugi su iskazali svoje neslaganje sa ovim postupkom govoreći da “ jedino ako je siguran da su mu pretpostavke tačne i može da ih potkrepi dokazima, samo tada bi trebao to da učini“. Pomešana mišljenja prema odnosu ka ljudskim životima potiču iz razmatranja potreba pojedinaca u poređenju sa potrebama društva. Učesnici smatraju da bismo sa jedne strane trebali voditi računa o dobrobiti društva, a sa druge strane o šteti koja se može naneti životima učesnika u eksperimentu. „ Sa jedne strane je zabranjeno igrati se sa životima drugih, osim ako cilj i način njegovog ostvarenja nisu potpuno jasni“. „ Iz istog razloga zbog kog su njegovi prijatelji pristali i znali da će im životi bitiu opasnosti, tako svako želi da njegovo ime bude povezano sa naučnim napretkom u sprečavanju smrti drugih“. Iako isprva, „ on nije imao saglasnost drugih, ali su oni smatrali da im to može pomoći na neki način“. Drugi su, uzevši u obzir moral, rekli da izlaganje opasnosti zavisi od pristanka individualca. Čak i ako izabere da to sprovede u delo, mora povesti računa da izbegne nanošenje štete drugima: „ Zavisi da li je vakcinisao svoje prijatelje, ili im je dao lek“, „ moramo proveriti sve druge opcije i tad možemo pitati da li je učinio sve da izbegne opasni fenomen“. Oni koji su protiv ugrožavanja pojedinaca tvrde da “ Trebao je da ugrozi samo sebe ili da bude zadovoljan svojom hipotezom koja nije dokazana“. Drugi učesnici su, na etičkoj osnovi, protiv sprovođenja ikakvih eksperimenata na ljudima: „ nije etično, niti moralno; zabranjeno je sprovoditi eksperimente na ljudima, samo na grupama ćelija“. „ Trebali su vršiti testove na životinjama“. Određena razmišljanja vezana za jevrejski zakon( „ kršenje glavnih principa jevrejskog zakona“) i strah od širenja problema ( mnoštvo zaraženih virusom, ako dođe do propusta“) naročito su bili prisutni kod religioznih učesnika. Grupa B: Identifikacija sa naučnikom Frankenštajnom i njegovim naučnim radom. Ovaj deo studije je sproveden u formi diskusija koje su dozvoljavale interakciju među učesnicima. Roman Frankenštajn ima dve interpretacije: knjiga stavlja naglasak na odgovornost naučnika i njegovu grešku u zapostavljanju sopstvene kreacije. Film sa druge strane stavlja naglasak na to da je naučnikova greška u izigravanju Boga u pokušaju da kreira život. Učesnici su suočeni sa filmom, koji je ostavio utisak na njih, što je dodatno pojačano činjenicom da su neki od njih religiozni. Samo je nekolicina studenata pravdala Frankenštajnovo ponašanje. Najčešće učeničko objašnjenje naučničkih postupaka je želja da se suoče sa smrću: „ On želi da oživi ljude zato da oni ne bi umrli“. „ Rekao je da želi da može da stvara delove tela od zamenskih delova“. Takođe je spomenuta emotivna (lična) motivacija, koja potiče iz traume koju je doživeo kad mu je majka umrla: „ Zato da bi mogao da oživi svoju majku“. Nekolicina studenata spomenula je njegovu želju da unapredi medicinu: „ Veoma je ambiciozan naučnik. Pokušava da istraži ono što niko do tada nije želeo, u cilju napretka nauke“. Drugi su spomenuli da bismo o postupcima trebali da sudimo prema cilju naučnika da unapredi društvo: „ Njegove namere su dobre, ali je postao suviše uzbuđen da bi i dalje vodio računa o profesorovim rečima da će rezultat biti izopačen i da se to ne bi smelo desiti“. Većina studenata se usprotivilo zamisli da ljudsko biće sme da kreira život, naročito zato što je to u suprotnosti sa božjim zakonima i ljudskim razumom: „ On se pretvara da je isti kao Bog i čini ono što ne treba da se čini, mislio je da ništa neće poći po zlu i onda na je na kraju ipak pošlo“. „ Nije prihvatio činjenicu da je smrt deo života,“ „ teško je razumeti da neko pokušava to da uradi jer je to u suprotnosti sa prirodnim zakonima“. Osećaj da je Frankenštajnovo ponašanje na „ivici“ ludila je takođe bilo uobičajeno: „ On je lud...mora da oživi ljude, da prikači na njih sve te stvari od delova tela drugih ljudi...nijedna normalna osoba ne bi to radila“. Jedan od učesnika je rekao da je Frankenštajnov slučaj izuzetan. „ Većina ljudi, bar onih sa sposobnostima naučnika, bi zaključila da nije dobra ideja oživljavati već umrle ljude. Frankenštajn je prava slika ludog, neodgovornog naučnika“. Drugi su mislili da su granice etike pomerene u filmu: “ Neetično je stvarati život jer je na kraju to živo biće kojeg se ne treba otarasiti na taj način. Na kraju mu se sveti“. „ To je već živo biće. On pokušava da ga ubije. Teško je sa gledišta morala“. ( sličan pogled na stvari postoji i u slučaju Crichtonovog Parka iz doba Jure). Nekolicina ih je istala nedostatak odgovornosti prema kreaturi koju je stvorio: „ Njegov problem je to što iako vidi da postoji problem on ne pokušava da ga reši. Umesto suočavanja sa tim, on ga napušta. To je neodgovorno“. „ To je kao zapostavljanje nekog ko je ozbiljno bolestan“. Mnoga razmišljanja u vezi sa Frankenštajnovim ponašanjem su ambivalentna: Iako si naučnikovi motivi ispravni, izvođenje nije: „ ovo delo je polufrankenštajnovsko: moraju se stvarati normalni ljudi koji dobro izgledaju. Ne možemo raditi polovično“, „ njegove namere su bile relativno dobre sa ciljem da pomogne doktoru i čovečanstvu, i kasnije je pokušao da sve popravi. Ali ipak malkice prekasno“. Poznaje metode eksperimenta ali mu nedostaje razumevanje:“ Imao je instrumente i naučno znanje ali se nalazio u zamci: Nije predvideo razorne posledice“, „ nije sumnjao u rezultat istraživanja, niti je slušao savete drugih“. Ova situacija je dovela do podele njegove ličnosti:“ On ima svoj normalan život i naučnika u sebi kog ne može da konroliše. Završiće eksperiment po cenu da dobije koleru, što je ipak preterano“. Tvrdoglavost u ostvarivanju cilja sprečava ga da vidi rezultat: „ Naučnik je pregledao profesorove beleške. Video je da se stvara izopačen čovek, ali je pomislio ¬„nema veze". Želi da ostvari cilj i nije ga briga šta će se desiti“. Diskusija Ova studija se bavi stavovima današnjih učenika prema nauci i naučnicima, na osnovu predstava o njima koje postoje u nekim delima klasične i popularne literature koja su uticala na savremenu kulturu. Inovativnost ovog istraživanja leži u testiranju vrednosti i socijalnih aspekata koji su pratili nauku u prošlosti, kako u prošlosti tako i sada. Ovakvo viđenje nauke daje nam mogućnost da je vidimo onakvi kakva je bila u momentu ostvarenja svakog od tih dostignuća. Klasična literatura je uvek pratila granice nauke i oslikavala strah i strepnju od svakog novog dostignuća. Bila je to kosmologija u vreme Galileja, struja u vreme izuma Frenklina i Volte, hemija krejem 19. veka i atomska bomba sredinom dvadesetog. Sada su to kloniranje i zloupotreba droga, koji su prisutni i kod današnjih učenika. Projekat je sproveden među visokoobrazovanim, pismenim i visoko motivisanim srednjoškolcima. Pretpostavili smo da su ove kvalifikacije neophodne za ostvarivanje cilja ovog istraživanja. To se ispostavilo tačnim, rezultati razlučitih alata korištenih u istraživanju: upitnik, eseji i intervjui oslikavali su intenzivan misaoni proces učesnika i njihovu svest o složenosti pokrenutih pitanja. Učesnici su iskazali sirok stepen razumevanja i konflikata u vezi sa sudom o nauci i naučnicima. Učesnici su odgovorili na izazove konflikata sa kojima su suočeni i izrazili su sopstvene u vezi sa iznetim problemima. Današnji učenici su osetljivi prema motivima koji iskazuju ambivalentnost i negativne predstave o nauci nađene u literaturi, iako su udaljeni bezmalo 300 godina od nastanka nekih od njih. Prirodno, način na koji veliki pisac podržava svoje ideje je drugačiji od načina na koji to prosečan srednjoškolac čini, Ovakav stav prema nauci kao pretnji nađen je i kod velikih pisaca i kod današnjih učenika. Horor koji je pokrenut velikom strašću prema nauci u vreme Dr Fausta (1480-1540), zbog upotrebe magije i veštičarenja u ostvarenju tog znanja, zamenjen je strahom od samog znanja, uglavnom od razornih rezultata čuvanja tog znanja i njegove tehnološke primene. O ovome se govori u drami Fizičar (Durrenmatt, 1988) za pozadinom straha od opasnih tehnoloških dostignuća kao što je atomska bomba. Stavovi današnjih đaka prema znanju su uglavnom ambivalentni-iskazuju divljenje prema nauci pomešano sa izrazima kritike i straha. Slika naučnika kao asocijalne i osobe bez emocija podseća na Sviftova pisanja ( 1726/1998), koji je okarakterisao naučnike kao osobe koje imaju toliko „uzvišene i zadivljujuće misli da ne mogu ni o čemu sa njim da komuniciraju“( st.160). Isto mišljenje je imala i jedanaestogodišnja devojčica iz Saganove knjige (1997), koja je naučnika opisala kao „štrebera“, takođe i kod Songa i Kima, kao i učesnici u ovom istraživanju u svojim opisima: Sa jedne strane, delo( naučnikovo) ima za cilj unapređenje čovečanstva, dok s druge strane, moramo biti skeptični prema naučnikovoj ulozi kao živog bića, jer na kraju ljudsko biće bi trebalo da funkcioniše kao ljudsko biće. Ovakva viđenja i dalje postoje uprkos činjenici da se nauka posmatra kao praktična, nasuprot Sviftovom viđenju. Dok je Svift 1726 smatrao da nauka nije efikasna u regulisanju naših života, učenici vide nauku kao korisnu za društvo. Đaci povezuju divljenje prema nauci sa strahom od nje, kritikuju naučnike kao štrebere i daju moralni osudu njihovih dostignuća i načina na koji su ostvarena. Moralna osuda je nađena u viđenjima đaka koji su gledali film Frankenštajn , iako su neki pravdali naučnika time da doprinosi napretku nauke uopšte, a posebno medicine, ili time da je bio motivisan akutnom ličnom traumom. Većina se usprotivila njegovom delu i rekla da je u suprotnosti sa naučnok etikom vodeći ka neizbrisivim posledicama jer se ne sme ubijati živo biće nakon što je stvoreno, iako je oštećeno. Drugi su istakli su i naučnik i eksperiment nelogični i na ivici ludila. Drugi su kritikovali njegovu neodgovornost prema ishodu njegove kreacije: „ umesto da brine o njemu, on ga zapostavlja. Umesto da se izbori sa njim, on odustaje“. Zanimljivo je spomenuti da učesnici ne pripisuju opasnost elektricitetu, oni već znaju da je eksperiment u knjizi Mary Shelley pogrešan i većina odbacuje ideju kloniranja kojoj se protive kad su direktno o njoj upitani, govoreći da je „ pogrešno stvarati život igrajući se Boga“. Kritika zasnovana na moralnim razlozima nađena je u vezi sa protagonistom radnje u Lovcima na mikrobe Naučnikom Reed-om, koji je ispitivao virus Žute groznice. Dok se nekolicina učesnika postovećuje sa retorikom autora knjige koji opravdava naučno istraživanje i odnosi se prema njemu sa divljenjem, mnogi učesnici su kritikovali Reed-a. Data su različita objašnjenja- vršiti eksperimente na ljudskim bićima se ne može opravdati, oni nisu na to pristali, eksperiment je mogao poći po zlu i učesnici i ostatak sveta bi bili ugroženi. Mogući su i drugi načini, moralniji, za izvođenje eksperimenata. Učesnici su takođe pitali da li su sve opcije uzete u obzir i koliki je naučnikov stepen sigurnosti u njegovu teoriju. Različiti ishodi u vezi sa predstavom o naučniku naglašeni su rezultatima upitnika sa unapred datim odgovorima ( slaganje oko ličnih osobina naučnika). Lista kvaliteta koji su dodeljeni naučniku su : intelektualnost, marljivost, metodičnost, logičnost i posvećenost radu. Ovakva predstava se nalazi i u dve knige popularne nauke napisane od strane dva naučnika, Smoot (1995) i Shapley (1967), koji su sumirali mnoge godine rada. Kako god, ponuđene su i manje pozitivne osobine, kao što je podložnost uticaju savremenika. Učesnici su takođe bili neodlučni u vezi sa nivoom moralnosti naučnika i njegove sposobnosti da brine o drugima. Jedno od najvažnijih otkrića ove studije je ozbiljna sumnja učesnika u vezi sa stavovima prema različitim percepcijama koje su testirane. Nalazi različitih alata korištenih u ovoj studiji ukazuju na komplikovane prestave o naučniku. Nasuprot popularnoj predstavi o naučniku kao usamljenom asocijalnom istraživaču koji radi u laboratoriji i koristi opremu prikladnu za školske laboratorije, kao što su boce i epruvete, učesnici u ovoj studiji iskazali su pomešanu predstave o naučniku. Nekolicina ih je istakla da su naučnici izolovani od ma kakvog društvenog okvira, porodične strukture i interakcije u političkom svetu. Drugi su istakli da su naučnici povezani sa društvom, da imaju dobre komunikacione sposobnosti i da su otvoreni za drugačije poglede na stvari. Pored osobina vezanih za njihovu inteligenciju -tvrdoglavost i vrednoću, naučnici su predstavljeni kao entuzijastični, otvoreni za lepote prirode, dok neki poseduju i odlične društvene talente. Oni opisuju naučnike kojimogu da pokrenu velike grupe, da uvere ljude iz različitih oblasti da im pomognu u prikupljanju sredstava za realizaciju svojih istraživanja. Naučni i primenljivi zaključci Ovo je unikatna studija o evaluaciji stavova izraelskih studenata prema nauci i naučnicima. Preporučujemo da se sprovede dodatno istraživanje sa drugim grupama stanovništva koje nisu proučavane ovde, drugim rečima sa grupama stanovništva niskog nivoa pismenosti. To se može učiniti adaptacijom istraživačkih alata tako da odgovaraju ovim grupama stanovništva. Praktična primena nije bila najvažniji smisao ove studije. Njen doprinos većim delom leži u veličini uvida koji je omogućila u odnos između humanizma i nauke koji se pojavljuje u literaturi i stavovima današnjih đaka. Postoji nekoliko predloga. Mi sumnjamo da negativni stavovi prema nauci i naučnicima ponekad potiču iz neznanja. Kao rešenje preporučujemo da se učenici opskrbe dovoljnim znanjem da mogu da formiraju razumno mišljenje. Predlažemo dovođenje učenika u kontakt sa delima vezanim za nauku sličnim onima analiziranim u teoretskom delu. Dovođenje u kontakt sa popularnim naučnim knjigama napisanim od strane samih istraživača upoznaće učenike sa stvarnim naučnicima i njihovim delima. To će im ujedno i povećati motivaciju ( Smoot, 1995). Metoda grupnog intervjua pokazala se kao posebno prikladna populaciji koja je učestovala u ovom istraživanju. Preporučujemo da se ova metoda usvoji za obuku populacija sa sličnim karakteristikama. Takođe bi trebala biti usvojena za vrednosna pitanja vezana za nauku i naučnike, kao i druge etičke teme. Dodatak 1 Eseji o percepciji predstave o naučniku Forma A: Slobodan sastav-Napišite esej o vašoj predstavi o naučniku ili naučnici. Forma B: Zadata tema-Napišite esej na sledeću temu: U društvu postoje različite predstave o naučnicima. Neki ih vide kao ludake, opasne ili neodgovorne, a neki naglašavaju pozitivne karakteristike ličnosti naučnika i njihovog rada na unapređenju društva i nauke. Iskažite svoje mišljenje o ovim dvema različitim predstavama o naučnicima. Dodatak 2 Polustruktuirani grupni intervjui Grupa A Knjiga Lovci na mikrobe govori o naučniku po imenu Walter Reed koji je istraživao mogućnost prevencije virusa Žute groznice koji je usmrtio hiljade američkih vojnika pre više od sto godina. Kako bi dokazao da komarci prenose virus, a iz razloga što su životinje otporne na njega, Reed nije imao drugog izbora nego da pita troje svojih prijatelja koji su učestvovali u istraživanju da dozvole da ih komarci ubodu i dovedu u životnu opasnost. Po vašem mišljenju, da li je Reed ispravno postupio? Kako biste vi postupili u takvoj situaciji? Grupa B Film koji ste upravo pogledali govori o naučniku ( Frankenštajnu), koji opijen uspehom ignoriše upozorenja drugih i stvara stvorenje od delova drugih tela. Čudovište koje je stvorio optužuje ga za greh i naposletku okreće protiv njega. 1. Molim vas ocenite naučnikov postupak u ovom delu. Osvrnite se na njegovu ideju da stvara život ni iz čega. Koji su njegovi motivi? Kako biste opisali ideju? Kao pozitivnu ili negativnu? Da li je razmišljao logično? Osvrnite se i na pitanje njegove odgovornosti prema stvorenju koje je stvorio. 2. Iznesite svoje mišljenje o naučniku. Kom tipu naučnika pripada? Koje su njegove osobine? Kakvu predstavu o naučniku daje ovaj film?

Bachelor's degree - University of Novi Sad

Years of experience: 22. Registered at ProZ.com: Feb 2011.

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