Does “Science” Make You Moral? The Effects of Priming Science on Moral Judgments and Behavior
Bibliographic record
Abstract
BACKGROUND: Previous work has noted that science stands as an ideological force insofar as the answers it offers to a variety of fundamental questions and concerns; as such, those who pursue scientific inquiry have been shown to be concerned with the moral and social ramifications of their scientific endeavors. No studies to date have directly investigated the links between exposure to science and moral or prosocial behaviors. METHODOLOGY/PRINCIPAL FINDINGS: Across four studies, both naturalistic measures of science exposure and experimental primes of science led to increased adherence to moral norms and more morally normative behaviors across domains. Study 1 (n = 36) tested the natural correlation between exposure to science and likelihood of enforcing moral norms. Studies 2 (n = 49), 3 (n = 52), and 4 (n = 43) manipulated thoughts about science and examined the causal impact of such thoughts on imagined and actual moral behavior. Across studies, thinking about science had a moralizing effect on a broad array of domains, including interpersonal violations (Studies 1, 2), prosocial intentions (Study 3), and economic exploitation (Study 4). CONCLUSIONS/SIGNIFICANCE: These studies demonstrated the morally normative effects of lay notions of science. Thinking about science leads individuals to endorse more stringent moral norms and exhibit more morally normative behavior. These studies are the first of their kind to systematically and empirically test the relationship between science and morality. The present findings speak to this question and elucidate the value-laden outcomes of the notion of science.
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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 | 0.001 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".