Scientific consensus and social controversy: exploring relationships between students’ conceptions of the nature of science, biological evolution, and global climate change
Bibliographic record
Abstract
It is overwhelmingly acknowledged by the scientific community that evolution and global climate change (GCC) are undeniably supported by physical evidence. And yet, both topics remain politically contentious in the United States. It is thought that students’ conceptions of the nature of science (NOS) may be key factors in their attitudes towards evolution and GCC. Our study explored this hypothesis guided by the following questions: Do changes in NOS conceptions correlate with changes in attitudes towards evolution or GCC? If there are correlations, are they similar for evolution and GCC? What demographic factors affect these correlations? Previously-developed tools were used to measure students’ conceptions of the nature of science and attitudes towards evolution, while national public opinion poll questions were used to measure attitudes towards GCC. Demographic questions were produced to target factors thought to influence attitudes towards evolution or global climate change. Overall sample size was N = 620. Principle components analysis was used to determine which variables accounted for the most variation, and those variables were analyzed using correlation tests, ANOVA, and ANCOVA to test for significant correlations and interaction effects. Changes in students’ attitudes towards evolution and global climate change were both positively correlated with shifts in conceptions about the nature of science. Attitudes towards evolution were negatively correlated with religiosity. Knowledge of evolutionary science was positively correlated with attitudes towards evolution, but knowledge about GCC was not significantly correlated with attitudes towards GCC. The strongest correlates of GCC attitudes were political leanings. Findings support the hypothesis that a better understanding of NOS may lead to changes in attitudes towards politically contentious ideas that are not scientifically contentious. Though attitudes towards evolution correlated strongly and significantly with a number of other factors including knowledge of evolutionary science and religiosity, expected non-political correlates with attitudes towards GCC were absent. Giving students a good conception of the modern nature of science may lead to views that are closer to those of the scientific community. This study provides novel evidence of a linkage between student acceptance of evolution and attitudes towards GCC, that is, NOS conceptions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".