Scientific Literacy, Attitudes towards Science, Religiosity and Superstitious Beliefs in the Romanian Context
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The present work, “Relationships between Scientific Literacy, Attitudes towards Science, Religiosity and Superstitious Beliefs in Romanian Context” is based on the first major research conducted in Romania concerning the public understanding of science paradigm. The research took place in the summer of 2009 and is representative for the Romanian, adult population. The information gathered concerns scientific literacy, attitudes towards science, medical knowledge, superstitious beliefs and religiosity. The coexistence of apparently opposed dimensions can be often seen in the Romanian public space. An example is the last presidential elections when the candidate that lost the elections explained his failure through pseudoscientific phenomenon. In this context the authors of this presentation will try to see the relationship between scientific knowledge, attitudes concerning science, superstitious beliefs and religiosity in contemporary Romanian society. One of our hypothesis is that religiosity will not be positively associated with superstitious beliefs. We’d expect that people who explain the events around them through Divinity would not believe that number 13 will bring bad luck or that if your left hand itches you will receive money (examples of common superstitions). Another hypothesis regards religiosity and its relation with scientific knowledge. We suppose that people who are more religious would have lower score of scientific literacy and, perhaps, a negative attitude towards science. Finally we will take into consideration the relationship between scientific knowledge and superstitious beliefs. We presume that those who have a high score in the scientific knowledge scale will be less superstitious.
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Scientific Literacy, Attitudes towards Science, Religiosity and Superstitious Beliefs in the Romanian Context
- 1. Scientific Literacy, Attitudes towards Science, Religiosity and Superstitious Beliefs in the Romanian Context Eugen Glăvan AlexandruCernat University of Bucharest Science and the Public 2010, London
- 3. Who are we ? Science and public in the Romanian context
- 4. ROU-UE comparison on macro-economic indicators;
- 5. ROU-UE comparison on scientific literacy scale;
- 6. ROU-UE comparison on attitudes scale.
- 8. Finance: National Authority for Scientific Research, PNII-CDI
- 9. Main themes: knowledge about and attitudes towards science and technology; medical knowledge; religious beliefs.
- 10. Coverage: dates of fieldwork: June-August 2009; observation unit: individuals; universe sampled: national; population: 1161citizens aged over 18 residing in Romania.
- 11. Methodology: time dimension: cross-sectional study; sampling procedures: the sampling design was multistadial stratified with clusters selected in the last stage (representative for the population of ROU); respondents wore selected through random root; method of data collection: face-to-face interview; weighting: weighting used (it was computed based on education and size of locality).
- 12. Software: SPSS (PASW v.18), Mplus v.5
- 14. The first major research conducted in Romania related to “the public understanding of science paradigm”. We are looking at relationships between scientific knowledge, attitudes towards science, superstitious beliefs and religiosity in contemporary Romanian society. Hypothesis 1: religiosity would not be positively associated with superstitious beliefs. We’d expect that people who explain the events around them through Divinity would not believe that number 13 will bring bad luck or that if your left hand itches you will receive money (examples of common superstitions). Hypothesis 2: people with a high score on the scientific knowledge scale are less superstitious. Hypothesis 3: people who are more religious have lower scores of scientific literacy Hypothesis 4: people who have more scientific knowledge have more positive attitude towards science 4 Research questions / Hypotesis
- 15. 5 Context: Who are we? Macro-economic indicators Lowest exports of high technology products as a share of total exports: 3.846
- 16. 6 Measurement of Scientific literacy dimension
- 17. 7 Comparing Scientific Literacy Scores Deficit model: better scientific literacy leads to positive attitudes towards science Cultural principles or beliefs and areas of science (Pardo & Calvo, 2004) Weak correlation between CS and positive attitudes (Allum, 2008)
- 18. 8 Measurement of Attitudes dimensions Idealism Risks Benefits Lack of utility
- 19. 9 SEM of Attitudes dimensions
- 20. SEM of Attitudes dimensions 10
- 21. 11 Measurement of superstitious and religious dimensions (I)
- 22. 12 Measurement of superstitious and religious dimensions (II)
- 23. Structural Equations Model (SEM) Idealism Scientific Literacy Index Lack of Utility Chi-Square: 3407.596; d.f.: 187; p: 0.000 CFI: 0.948 RMSEA: 0.029 Religious Practices Risks Positive relation Religious Faith Negative relation Benefits Super-stitious Science vs. Religion 13 Hypothesis 1: religiosity would not be positively associated with superstitious beliefs. (partially) Hypothesis 2: people with a high score on the scientific knowledge scale are less superstitious. (confirmed) Hypothesis 3: people who are more religious have lower scores of scientific literacy. (confirmed) Hypothesis 4: people who have more scientific knowledge have more positive attitude towards science. (partially)
- 24. BibliographyConclusions Albarracin, D., Johnson, B. T., & Zanna, M. P. (2005). The handbook of attitudes. Mahwah: Lawrence Erlbaum Associates. Allum, N., Sturgis, P., Tabourazi, D., & Brunton-Smith, I. (2008). Science knowledge and attitudes across cultures: a meta-analysis. Public Understanding of Science, 17, 35-54. Bauer, M. W., Petkova, K., & Boyadjieva, P. (2000). Public Knowledge of and Attitudes to Science: Alternative Measures That May End the "Science War". Science, Technology, & Human Values, 25 (1, Winter), 30-51. Gauchat, G. W. (2008). A Test of Three Theories of Anti-Science Attitudes. Sociological Focus, 41 (Nov. 4), 337-357. Lidskog, R. (1996). In Science We Trust? On the Relation Between Scientific Knowledge, Risk Consciousness and Public Trust. ActaSociologica, 39, 31-56. Miller, J. D. (2004). Public understanding of, and attitudes toward, scientific research: what we know and what we need to know. Public Understanding of Science, 13, 273–294 Oskamp, S., & Schultz, P. W. (2005). Attitudes and Opinions. Mahwah: Lawrence Erlbaum Associates. Pardo, R., & Calvo, F. (2004). The Cognitive Dimension of Public Perceptions of Science: Methodological Issues. Public Understanding of Science, 13 (3), 203-227. Pinch, T. J., & Bijker, W. E. (1984). The Social Construction of Facts and Artefacts: Or How the Sociology of Science and the Sociology of Technology Might Benefit Each Other. Social Studies of Science, 14 (3, Aug.), 399-441. Pion, G. M., & Lipsey, M. W. (1981). Public Attitudes Toward Science and Technology: What Have the Surveys Told Us? The Public Opinion Quarterly, 45 (3 Autumn), 303-316 Sample, J., & Warland, R. (1973). Attitude and Prediction of Behavior. Social Forces, 51 (3, Mar.), 292-304. Shanahan, J., Scheufele, D., & Lee, E. (2001). Trends: Attitudes about Agricultural Biotechnology and Genetically Modified Organisms. The Public Opinion Quarterly, 65 (no. 2, Summer), 267-281 *** European Barometer 2005 *** European Barometer 2010
- 25. Thank You Eugen Glăvan University of Bucharest Faculty of Sociologyand Social Work glavan.eugen@gmail.com Alexandru Cernat University of Bucharest Faculty of Sociologyand Social Work cernat.alex@gmail.com
Editor's Notes
- ContextualizareComparaţie România - restul Europei pe scala de cunoaştere ştiinţificăComparaţie România - restul Europei pe câţiva indicatori macro-economici (pentru a vedea care este relaţia dintre 1 şi)Comparaţie România – restul Europei pe dimensiunea atitudinilor faţă de ştiinţă (pentru a testa ipoteza de relaţie în formă de U întors dintre stocul de cunoaştere ştiinţifică şi atitudine faţă de ştiinţă, relaţie mediată de gradul dezvoltării ţării)Although the original Lisbon Strategy target of 3% GDP to be spent on R&D has notbeen met, tangible benefits are nonetheless apparent. (EurobarometerScienceand Technology 2010)Percentage of households who have Internet access at home: 30% (2009, Eurostat); lowest Bulgaria 30%Exports of high technology products as a share of total exports: 3.846Gross domestic product (GDP) is a measure for the economic activity. It is defined as the value of all goods and services produced less the value of any goods or services used in their creation. The volume index of GDP per capita in Purchasing Power Standards (PPS) is expressed in relation to the European Union (EU-27) average set to equal 100. If the index of a country is higher than 100, this country's level of GDP per head is higher than the EU average and vice versa. Basic figures are expressed in PPS, i.e. a common currency that eliminates the differences in price levels between countries allowing meaningful volume comparisons of GDP between countries. Please note that the index, calculated from PPS figures and expressed with respect to EU27 = 100, is intended for cross-country comparisons rather than for temporal comparisons. Gross domestic expenditure on R&D The four indicators provided are GERD (Gross domestic expenditure on R&D) as a percentage of GDP, percentage of GERD financed by industry, percentage of GERD financed by government and percentage of GERD financed from abroad. "Research and experimental development (R&D) comprise creative work undertaken on a systematic basis in order to increase the stock of knowledge, including knowledge of man, culture and society and the use of this stock of knowledge to devise new applications" (Frascati Manual, 2002 edition, § 63 ). R&D is an activity where there are significant transfers of resources between units, organisations and sectors and it is important to trace the flow of R&D funds.
- Cum explicăm diferenţele dintre STISOC şi EB 2010?Sunt procentele La indicicele de cunoasterestiintifica am pus doaritemiinegativi. Acestiadadeau alt factor in analizaexploratoriesiimpreuna au un goodness of fit bun. Si teoreticeisuntmaibunipentru ca nu suntinfluentati de tendinta de a fi de acord. Tendinţa de a fi de acordPinch & BijkerToată cunoaşterea trebuie abordată ca fiind social construită: explicaţiile pentru geneza, acceptarea sau respingerea [knowledge-claims] reprezintă domeniul lumii sociale şi nu cel al lumii naturale. (Pinch & Bijker, 1984b, p. 401)Pardo & CalvoRolul cunoaşterii sau familiaritatea cu ştiinţa este în mod particular importantă nu ca predictor al atitudinilor generale faţă de ştiinţă, ci ca bază pentru diferenţierea atitudinilor pentru diverse arii ale ştiinţei şi tipuri de cercetare. (Evans şi Durant, 1995) (Pardo & Calvo, 2004, p. 204) de aici rezultând „modelul de deficit”.Hartă a ariilor ştiinţei şi tehnologiei problematice sau neutre – ce principii şi credinţe culturale se ciocnesc cu anumite arii ale ştiinţei şi tehnologiei într-o ţară dată.(Pardo & Calvo, 2004, pp. 209-210)Allumşi alţii - critica model deficit“deficit model” of PUS has shown that a simple, positive, linear relationship between attitudes and knowledge about science under all circumstances is an over-simplification. Firstly, although there are many studies that show positive correlations between general attitudes and knowledge, little systematic evidence exists about the association between different subsets of scientific knowledge (e.g. genetic, environmental) and specific technologies (e.g. biotechnology, nuclear power). Secondly, not all published studies report correlations net of possible confounding variables like education and gender, without which any causal explanations must be viewed with particular suspicion. Thirdly, little is known about cross-cultural variation in the strength of correlations outside of Europe and the US and what might account for such variation if it is present. (Allum, et al., 2008, pp. 39-40)Bauer şi alţii – modelul cultural al trecerii de la societăţile industriale la cele postindustriale: In this view, the shift from industrial to post-industrial society (Inglehart, 1990) is accompanied by changes in the relation between science, society and the public. At the industrial stage of development, science is idealized as the preferred route to economic expansion and social emancipation and the more citizens know about science, the more their attitudes conform to this stereotype. In post-industrial societies, science is taken for granted, knowledge becomes more specialized and a more skeptical and questioning public views science with greater suspicion, while expecting it to continue to deliver prosperity. In this situation, more knowledge can equally lead to greater skepticism as to optimism, due to the lack of a positive cultural stereotype for science. (Allum, et al., 2008, p. 37)Allum – model culturalItemii atitudinali includ cinci arii de interes:1. Ştiinţa în general2. Puterea nucleară3. Medicina genetică4. Mâncarea modificată genetic5. Ştiinţa mediuluiThe basic OLS regression model took the form:Y= b0 + b1X1+ b2X2 + b3X3 + b4X4 + ewhere Y = attitude to science, X1 = scientific knowledge, X2 = gender, X3 = age in years, andX4 = educational level.When the breadth of this meta-analysis is considered, spanning 15 years, 40 countries and 193 studies, we consider this prima facie evidence for the existence of a stable positive relationship between science literacy and attitudes to science and technology. In fact, there is no effect even if one fits the model without the other macro-level variables. This runs counter to expectations derived from the “two cultures” thesis. (Allum, et al., 2008, p. 50)Pion & LipseyScience and technology run together in an un-differentiated concept dominated by images of everyday medicine, industry, and household appliances. Scientists themselves are seen in stereotyped terms that more nearly describe comic book characters or computers than real people, and the core notion of basic scientific research hardly figures in the conception at all. (Pion & Lipsey, 1981, p. 314)Shanahan, et al. In terms of attitude, the major issue is whether the risks of GMOs outweigh their perceived benefits. (Shanahan, et al., 2001, p. 269)Negative items were grouped in exploratory factor analysis:together they have a better goodness of fit;theoretically they are better because isn’t influenced by tendency of approvalRMSEA (Root Mean Square Error of Approximation) This measure is based on the non-centrality parameter. Its formula can be shown to equal: √[([χ2/df] - 1)/(N - 1)]where N the sample size and df the degrees of freedom of the model. (If χ2 is less than df, then RMSEA is set to zero.) Good models have an RMSEA of .05 or less. Models whose RMSEA is .10 or more have poor fit. A confidence interval can be computed for this index. First, the value of the non-centrality parameter is determined by χ2 - df. The confidence interval for non-centrality parameter can be determined for χ2, df, and the width of the confidence interval. (One can use the function "CNONCT" within SAS to compute these values. Also a website for computing p values for the non-centrality parameter.) Then these values are substituted for χ2 - df into the formula for the RMSEA. Ideally the lower value of the 90% confidence interval includes or is very near zero and the upper value is not very large, i.e., less than .08. Note that the RMSEA can be misleading when the df are small and sample size is not large. For instance, a chi square of 2.098 (a value not statistically significant), with a df of 1 and N of 70 yields an RMSEA of .126. Chi Square to df Ratio: χ2/df (in table Chi-square (d.f.;p)) There are no consistent standards for what is considered an acceptable model. Cum comentezvalorile din tabel?Confirmatory factor analysis confirm (validate?) that the items load on each dimension and tests of goodness of fit shows that the models are good.
- Frecvenţe anumiteScientificliteracy: CS3, CS7, CS8, CS9, CS10, CS12, CS13In 2005 Romania has 51% correct responses (+7 from 2002) Eurobarometer Science 2005, p. 42Cum explicăm creşterea foarte puternică în unele cazuri şi nu în altele?Definiţia „competenţei ştiinţifice” (scientificliteracy) – Shen: serii de măsuri separate cum ar fi rolurile cetăţeneşti, roluri de consumatori, nivel general de înţelegere culturală. National Academy of Engineering (NAE): măsură generală a competenţei tehnologice. Miller şi Kimmel (2001): măsură a competenţei biomedicale. (Miller, 2004, p. 275) the percentage of US adults who understand the basic idea of an experiment has increased from approximately 22 percent in 1993 to 35 percent in 1999. (Miller, 2004, p. 277)the proportion of US adults demonstrating an understanding of probability has remained unchanged throughout the 1990s (NSB, 2000).60% din americanii adulţi apreciază că astrologia nu este ştiinţifică (cercetări începând cu 1988).Throughout the last decade, approximately 15 percent of US adults have agreed with this statement, but more than 80 percent of Americans have disagreed with the idea that science is not important in their daily lives (NSB, 2000). Three-quarters of US adults agreed with this statement in 1985, and more than 80 percent have agreed with this view throughout the 1990s (NSB, 2000). (Miller, 2004, p. 285)Cele mai comune îngrijorări sunt faptul că ştiinţa şi tehnologia fac viaţa să se schimbe prea repede şi impactul valorilor tradiţionale şi religioase şi a credinţei. Concluzii - it is clear that the best long-term strategy for increasing civic scientific literacy is to improve pre-collegiate education so that all students who graduate from college are scientifically literate. (Miller, 2004, p. 290)The proportion of US adults qualifying as being scientifically literate has doubled over the last two decades, but the current level is still problematic for a democratic society that values citizen understanding of major national policies and participation in the resolution of important policy disputes. (Miller, 2004, p. 273)The empirical evidence from most of these studies points to a weak correlation between knowledge about scientific facts and processes and positive attitudes towards science. (Allum, et al., 2008, p. 36)Bauer şi alţii – modelul ideology of science: we are proposing a metric scale for measuring institutional knowledge of science. Twelve items cover issues from teamwork, peer review, funding, prestige, autonomy, science policy, and international competitiveness to a country's science base. (Bauer, et al., 2000, p. 32)Puncte de vedere tradiţional-idealiste sau realist-sceptice. A potentiallyvaluableapproachhasbeenthereview of thedifferential formal properties of theknowledge scale in a number of countries (i.e. its formal consistencyestimatedthroughCronbach’salphacoefficient), andthesearch for theoretical—notmethodological or statistical—explanationswhich, in theview of theirauthors, pointtodifferences in thedistribution of knowledgeandignorance in European societies as a function of the diverse socioeconomic and cultural characteristics of countrieslocated at differentstages in thetransitionfrom industrial to postindustrial societies, on which more later (see Bauer et al., 1994; Durant et al., 2000).Hartă a ariilor ştiinţei şi tehnologiei problematice sau neutre – ce principii şi credinţe culturale se ciocnesc cu anumite arii ale ştiinţei şi tehnologiei într-o ţară dată.(Pardo & Calvo, 2004, pp. 209-210)
- Cum explicăm diferenţele dintre STISOC şi EB 2010?Chiar daca Cronbach este mic, analizele factoriale confirmatotrii confirma ca itemii se intarca pe cate o dimensiune si ca testele de goodness of fit arata ca modelele sunt bune.Pinch & BijkerToată cunoaşterea trebuie abordată ca fiind social construită: explicaţiile pentru geneza, acceptarea sau respingerea [knowledge-claims] reprezintă domeniul lumii sociale şi nu cel al lumii naturale. (Pinch & Bijker, 1984b, p. 401)Pardo & CalvoRolul cunoaşterii sau familiaritatea cu ştiinţa este în mod particular importantă nu ca predictor al atitudinilor generale faţă de ştiinţă, ci ca bază pentru diferenţierea atitudinilor pentru diverse arii ale ştiinţei şi tipuri de cercetare. (Evans şi Durant, 1995) (Pardo & Calvo, 2004, p. 204) de aici rezultând „modelul de deficit”.Hartă a ariilor ştiinţei şi tehnologiei problematice sau neutre – ce principii şi credinţe culturale se ciocnesc cu anumite arii ale ştiinţei şi tehnologiei într-o ţară dată.(Pardo & Calvo, 2004, pp. 209-210)Allumşi alţii - critica model deficit“deficit model” of PUS has shown that a simple, positive, linear relationship between attitudes and knowledge about science under all circumstances is an over-simplification. Firstly, although there are many studies that show positive correlations between general attitudes and knowledge, little systematic evidence exists about the association between different subsets of scientific knowledge (e.g. genetic, environmental) and specific technologies (e.g. biotechnology, nuclear power). Secondly, not all published studies report correlations net of possible confounding variables like education and gender, without which any causal explanations must be viewed with particular suspicion. Thirdly, little is known about cross-cultural variation in the strength of correlations outside of Europe and the US and what might account for such variation if it is present. (Allum, et al., 2008, pp. 39-40)Bauer şi alţii – modelul cultural al trecerii de la societăţile industriale la cele postindustriale: In this view, the shift from industrial to post-industrial society (Inglehart, 1990) is accompanied by changes in the relation between science, society and the public. At the industrial stage of development, science is idealized as the preferred route to economic expansion and social emancipation and the more citizens know about science, the more their attitudes conform to this stereotype. In post-industrial societies, science is taken for granted, knowledge becomes more specialized and a more skeptical and questioning public views science with greater suspicion, while expecting it to continue to deliver prosperity. In this situation, more knowledge can equally lead to greater skepticism as to optimism, due to the lack of a positive cultural stereotype for science. (Allum, et al., 2008, p. 37)Allum – model culturalItemii atitudinali includ cinci arii de interes:1. Ştiinţa în general2. Puterea nucleară3. Medicina genetică4. Mâncarea modificată genetic5. Ştiinţa mediuluiThe basic OLS regression model took the form:Y= b0 + b1X1+ b2X2 + b3X3 + b4X4 + ewhere Y = attitude to science, X1 = scientific knowledge, X2 = gender, X3 = age in years, andX4 = educational level.When the breadth of this meta-analysis is considered, spanning 15 years, 40 countries and 193 studies, we consider this prima facie evidence for the existence of a stable positive relationship between science literacy and attitudes to science and technology. In fact, there is no effect even if one fits the model without the other macro-level variables. This runs counter to expectations derived from the “two cultures” thesis. (Allum, et al., 2008, p. 50)Pion & LipseyScience and technology run together in an un-differentiated concept dominated by images of everyday medicine, industry, and household appliances. Scientists themselves are seen in stereotyped terms that more nearly describe comic book characters or computers than real people, and the core notion of basic scientific research hardly figures in the conception at all. (Pion & Lipsey, 1981, p. 314)Shanahan, et al. In terms of attitude, the major issue is whether the risks of GMOs outweigh their perceived benefits. (Shanahan, et al., 2001, p. 269)
- Cum explicăm diferenţele dintre STISOC şi EB 2010?Chiar daca Cronbach este mic, analizele factoriale confirmatotrii confirma ca itemii se intarca pe cate o dimensiune si ca testele de goodness of fit arata ca modelele sunt bune.Pinch & BijkerToată cunoaşterea trebuie abordată ca fiind social construită: explicaţiile pentru geneza, acceptarea sau respingerea [knowledge-claims] reprezintă domeniul lumii sociale şi nu cel al lumii naturale. (Pinch & Bijker, 1984b, p. 401)Pardo & CalvoRolul cunoaşterii sau familiaritatea cu ştiinţa este în mod particular importantă nu ca predictor al atitudinilor generale faţă de ştiinţă, ci ca bază pentru diferenţierea atitudinilor pentru diverse arii ale ştiinţei şi tipuri de cercetare. (Evans şi Durant, 1995) (Pardo & Calvo, 2004, p. 204) de aici rezultând „modelul de deficit”.Hartă a ariilor ştiinţei şi tehnologiei problematice sau neutre – ce principii şi credinţe culturale se ciocnesc cu anumite arii ale ştiinţei şi tehnologiei într-o ţară dată.(Pardo & Calvo, 2004, pp. 209-210)Allum şi alţii - critica model deficit“deficit model” of PUS has shown that a simple, positive, linear relationship between attitudes and knowledge about science under all circumstances is an over-simplification. Firstly, although there are many studies that show positive correlations between general attitudes and knowledge, little systematic evidence exists about the association between different subsets of scientific knowledge (e.g. genetic, environmental) and specific technologies (e.g. biotechnology, nuclear power). Secondly, not all published studies report correlations net of possible confounding variables like education and gender, without which any causal explanations must be viewed with particular suspicion. Thirdly, little is known about cross-cultural variation in the strength of correlations outside of Europe and the US and what might account for such variation if it is present. (Allum, et al., 2008, pp. 39-40)Bauer şi alţii – modelul cultural al trecerii de la societăţile industriale la cele postindustriale: In this view, the shift from industrial to post-industrial society (Inglehart, 1990) is accompanied by changes in the relation between science, society and the public. At the industrial stage of development, science is idealized as the preferred route to economic expansion and social emancipation and the more citizens know about science, the more their attitudes conform to this stereotype. In post-industrial societies, science is taken for granted, knowledge becomes more specialized and a more skeptical and questioning public views science with greater suspicion, while expecting it to continue to deliver prosperity. In this situation, more knowledge can equally lead to greater skepticism as to optimism, due to the lack of a positive cultural stereotype for science. (Allum, et al., 2008, p. 37)Allum – model culturalItemii atitudinali includ cinci arii de interes:1. Ştiinţa în general2. Puterea nucleară3. Medicina genetică4. Mâncarea modificată genetic5. Ştiinţa mediuluiThe basic OLS regression model took the form:Y = b0 + b1X1+ b2X2 + b3X3 + b4X4 + ewhere Y = attitude to science, X1 = scientific knowledge, X2 = gender, X3 = age in years, andX4 = educational level.When the breadth of this meta-analysis is considered, spanning 15 years, 40 countries and 193 studies, we consider this prima facie evidence for the existence of a stable positive relationship between science literacy and attitudes to science and technology. In fact, there is no effect even if one fits the model without the other macro-level variables. This runs counter to expectations derived from the “two cultures” thesis. (Allum, et al., 2008, p. 50)Pion & Lipsey Science and technology run together in an un-differentiated concept dominated by images of everyday medicine, industry, and household appliances. Scientists themselves are seen in stereotyped terms that more nearly describe comic book characters or computers than real people, and the core notion of basic scientific research hardly figures in the conception at all. (Pion & Lipsey, 1981, p. 314)Shanahan, et al. In terms of attitude, the major issue is whether the risks of GMOs outweigh their perceived benefits. (Shanahan, et al., 2001, p. 269)
- Cum explicăm diferenţele dintre STISOC şi EB 2010?Chiar daca Cronbach este mic, analizele factoriale confirmatotrii confirma ca itemii se intarca pe cate o dimensiune si ca testele de goodness of fit arata ca modelele sunt bune.Pinch & BijkerToată cunoaşterea trebuie abordată ca fiind social construită: explicaţiile pentru geneza, acceptarea sau respingerea [knowledge-claims] reprezintă domeniul lumii sociale şi nu cel al lumii naturale. (Pinch & Bijker, 1984b, p. 401)Pardo & CalvoRolul cunoaşterii sau familiaritatea cu ştiinţa este în mod particular importantă nu ca predictor al atitudinilor generale faţă de ştiinţă, ci ca bază pentru diferenţierea atitudinilor pentru diverse arii ale ştiinţei şi tipuri de cercetare. (Evans şi Durant, 1995) (Pardo & Calvo, 2004, p. 204) de aici rezultând „modelul de deficit”.Hartă a ariilor ştiinţei şi tehnologiei problematice sau neutre – ce principii şi credinţe culturale se ciocnesc cu anumite arii ale ştiinţei şi tehnologiei într-o ţară dată.(Pardo & Calvo, 2004, pp. 209-210)Allum şi alţii - critica model deficit“deficit model” of PUS has shown that a simple, positive, linear relationship between attitudes and knowledge about science under all circumstances is an over-simplification. Firstly, although there are many studies that show positive correlations between general attitudes and knowledge, little systematic evidence exists about the association between different subsets of scientific knowledge (e.g. genetic, environmental) and specific technologies (e.g. biotechnology, nuclear power). Secondly, not all published studies report correlations net of possible confounding variables like education and gender, without which any causal explanations must be viewed with particular suspicion. Thirdly, little is known about cross-cultural variation in the strength of correlations outside of Europe and the US and what might account for such variation if it is present. (Allum, et al., 2008, pp. 39-40)Bauer şi alţii – modelul cultural al trecerii de la societăţile industriale la cele postindustriale: In this view, the shift from industrial to post-industrial society (Inglehart, 1990) is accompanied by changes in the relation between science, society and the public. At the industrial stage of development, science is idealized as the preferred route to economic expansion and social emancipation and the more citizens know about science, the more their attitudes conform to this stereotype. In post-industrial societies, science is taken for granted, knowledge becomes more specialized and a more skeptical and questioning public views science with greater suspicion, while expecting it to continue to deliver prosperity. In this situation, more knowledge can equally lead to greater skepticism as to optimism, due to the lack of a positive cultural stereotype for science. (Allum, et al., 2008, p. 37)Allum – model culturalItemii atitudinali includ cinci arii de interes:1. Ştiinţa în general2. Puterea nucleară3. Medicina genetică4. Mâncarea modificată genetic5. Ştiinţa mediuluiThe basic OLS regression model took the form:Y = b0 + b1X1+ b2X2 + b3X3 + b4X4 + ewhere Y = attitude to science, X1 = scientific knowledge, X2 = gender, X3 = age in years, andX4 = educational level.When the breadth of this meta-analysis is considered, spanning 15 years, 40 countries and 193 studies, we consider this prima facie evidence for the existence of a stable positive relationship between science literacy and attitudes to science and technology. In fact, there is no effect even if one fits the model without the other macro-level variables. This runs counter to expectations derived from the “two cultures” thesis. (Allum, et al., 2008, p. 50)Pion & Lipsey Science and technology run together in an un-differentiated concept dominated by images of everyday medicine, industry, and household appliances. Scientists themselves are seen in stereotyped terms that more nearly describe comic book characters or computers than real people, and the core notion of basic scientific research hardly figures in the conception at all. (Pion & Lipsey, 1981, p. 314)Shanahan, et al. In terms of attitude, the major issue is whether the risks of GMOs outweigh their perceived benefits. (Shanahan, et al., 2001, p. 269)
- Cum explicăm diferenţele dintre STISOC şi EB 2010?Pinch & BijkerToată cunoaşterea trebuie abordată ca fiind social construită: explicaţiile pentru geneza, acceptarea sau respingerea [knowledge-claims] reprezintă domeniul lumii sociale şi nu cel al lumii naturale. (Pinch & Bijker, 1984b, p. 401)Pardo & CalvoRolul cunoaşterii sau familiaritatea cu ştiinţa este în mod particular importantă nu ca predictor al atitudinilor generale faţă de ştiinţă, ci ca bază pentru diferenţierea atitudinilor pentru diverse arii ale ştiinţei şi tipuri de cercetare. (Evans şi Durant, 1995) (Pardo & Calvo, 2004, p. 204) de aici rezultând „modelul de deficit”.Hartă a ariilor ştiinţei şi tehnologiei problematice sau neutre – ce principii şi credinţe culturale se ciocnesc cu anumite arii ale ştiinţei şi tehnologiei într-o ţară dată.(Pardo & Calvo, 2004, pp. 209-210)Allum şi alţii - critica model deficit“deficit model” of PUS has shown that a simple, positive, linear relationship between attitudes and knowledge about science under all circumstances is an over-simplification. Firstly, although there are many studies that show positive correlations between general attitudes and knowledge, little systematic evidence exists about the association between different subsets of scientific knowledge (e.g. genetic, environmental) and specific technologies (e.g. biotechnology, nuclear power). Secondly, not all published studies report correlations net of possible confounding variables like education and gender, without which any causal explanations must be viewed with particular suspicion. Thirdly, little is known about cross-cultural variation in the strength of correlations outside of Europe and the US and what might account for such variation if it is present. (Allum, et al., 2008, pp. 39-40)Bauer şi alţii – modelul cultural al trecerii de la societăţile industriale la cele postindustriale: In this view, the shift from industrial to post-industrial society (Inglehart, 1990) is accompanied by changes in the relation between science, society and the public. At the industrial stage of development, science is idealized as the preferred route to economic expansion and social emancipation and the more citizens know about science, the more their attitudes conform to this stereotype. In post-industrial societies, science is taken for granted, knowledge becomes more specialized and a more skeptical and questioning public views science with greater suspicion, while expecting it to continue to deliver prosperity. In this situation, more knowledge can equally lead to greater skepticism as to optimism, due to the lack of a positive cultural stereotype for science. (Allum, et al., 2008, p. 37)Allum – model culturalItemii atitudinali includ cinci arii de interes:1. Ştiinţa în general2. Puterea nucleară3. Medicina genetică4. Mâncarea modificată genetic5. Ştiinţa mediuluiThe basic OLS regression model took the form:Y = b0 + b1X1+ b2X2 + b3X3 + b4X4 + ewhere Y = attitude to science, X1 = scientific knowledge, X2 = gender, X3 = age in years, andX4 = educational level.When the breadth of this meta-analysis is considered, spanning 15 years, 40 countries and 193 studies, we consider this prima facie evidence for the existence of a stable positive relationship between science literacy and attitudes to science and technology. In fact, there is no effect even if one fits the model without the other macro-level variables. This runs counter to expectations derived from the “two cultures” thesis. (Allum, et al., 2008, p. 50)Pion & Lipsey Science and technology run together in an un-differentiated concept dominated by images of everyday medicine, industry, and household appliances. Scientists themselves are seen in stereotyped terms that more nearly describe comic book characters or computers than real people, and the core notion of basic scientific research hardly figures in the conception at all. (Pion & Lipsey, 1981, p. 314)Shanahan, et al. In terms of attitude, the major issue is whether the risks of GMOs outweigh their perceived benefits. (Shanahan, et al., 2001, p. 269)
- Cum explicăm diferenţele dintre STISOC şi EB 2010?Pinch & BijkerToată cunoaşterea trebuie abordată ca fiind social construită: explicaţiile pentru geneza, acceptarea sau respingerea [knowledge-claims] reprezintă domeniul lumii sociale şi nu cel al lumii naturale. (Pinch & Bijker, 1984b, p. 401)Pardo & CalvoRolul cunoaşterii sau familiaritatea cu ştiinţa este în mod particular importantă nu ca predictor al atitudinilor generale faţă de ştiinţă, ci ca bază pentru diferenţierea atitudinilor pentru diverse arii ale ştiinţei şi tipuri de cercetare. (Evans şi Durant, 1995) (Pardo & Calvo, 2004, p. 204) de aici rezultând „modelul de deficit”.Hartă a ariilor ştiinţei şi tehnologiei problematice sau neutre – ce principii şi credinţe culturale se ciocnesc cu anumite arii ale ştiinţei şi tehnologiei într-o ţară dată.(Pardo & Calvo, 2004, pp. 209-210)Allum şi alţii - critica model deficit“deficit model” of PUS has shown that a simple, positive, linear relationship between attitudes and knowledge about science under all circumstances is an over-simplification. Firstly, although there are many studies that show positive correlations between general attitudes and knowledge, little systematic evidence exists about the association between different subsets of scientific knowledge (e.g. genetic, environmental) and specific technologies (e.g. biotechnology, nuclear power). Secondly, not all published studies report correlations net of possible confounding variables like education and gender, without which any causal explanations must be viewed with particular suspicion. Thirdly, little is known about cross-cultural variation in the strength of correlations outside of Europe and the US and what might account for such variation if it is present. (Allum, et al., 2008, pp. 39-40)Bauer şi alţii – modelul cultural al trecerii de la societăţile industriale la cele postindustriale: In this view, the shift from industrial to post-industrial society (Inglehart, 1990) is accompanied by changes in the relation between science, society and the public. At the industrial stage of development, science is idealized as the preferred route to economic expansion and social emancipation and the more citizens know about science, the more their attitudes conform to this stereotype. In post-industrial societies, science is taken for granted, knowledge becomes more specialized and a more skeptical and questioning public views science with greater suspicion, while expecting it to continue to deliver prosperity. In this situation, more knowledge can equally lead to greater skepticism as to optimism, due to the lack of a positive cultural stereotype for science. (Allum, et al., 2008, p. 37)Allum – model culturalItemii atitudinali includ cinci arii de interes:1. Ştiinţa în general2. Puterea nucleară3. Medicina genetică4. Mâncarea modificată genetic5. Ştiinţa mediuluiThe basic OLS regression model took the form:Y = b0 + b1X1+ b2X2 + b3X3 + b4X4 + ewhere Y = attitude to science, X1 = scientific knowledge, X2 = gender, X3 = age in years, andX4 = educational level.When the breadth of this meta-analysis is considered, spanning 15 years, 40 countries and 193 studies, we consider this prima facie evidence for the existence of a stable positive relationship between science literacy and attitudes to science and technology. In fact, there is no effect even if one fits the model without the other macro-level variables. This runs counter to expectations derived from the “two cultures” thesis. (Allum, et al., 2008, p. 50)Pion & Lipsey Science and technology run together in an un-differentiated concept dominated by images of everyday medicine, industry, and household appliances. Scientists themselves are seen in stereotyped terms that more nearly describe comic book characters or computers than real people, and the core notion of basic scientific research hardly figures in the conception at all. (Pion & Lipsey, 1981, p. 314)Shanahan, et al. In terms of attitude, the major issue is whether the risks of GMOs outweigh their perceived benefits. (Shanahan, et al., 2001, p. 269)
- Model controlat cu variabilele din regresiiIf TLI (Tucker-Lewis Index) values were between 0.90 and 0.95, they indicated adequate fit. Values above 0.95 indicated good fit and values below 0.90 indicated poor fit.10,14CIF (Comparative Fit Index) idem TLI. Confirmatory factor analysis confirm (validate?) that the items load on each dimension and tests of goodness of fit shows that the models are good.