Between Law and Conscience: Act Legality Shapes Moral Evaluation
Bibliographic record
Abstract
Can legality shape moral judgment? Across four experiments (N = 1,559), participants judged identical actions as more morally wrong when they were described as illegal rather than “not illegal.” This effect extended to judgments of moral character: individuals who performed illegal actions were evaluated more negatively than those who performed otherwise identical legal actions. The influence of legality was robust across several contexts, emerging even when agents violated the law unintentionally, when laws were imposed by a totalitarian government, and when actions were described as socially accepted by the majority of local residents. At the same time, legality exerted a stronger influence when it aligned with prevailing social norms and among participants who more strongly endorsed respect for authority as a moral good. We propose that people commonly use legality as a heuristic cue when evaluating the morality of actions and actors. Together, these findings suggest that legal frameworks not only reflect public opinion but also shape how people judge the morality of actions and those who perform them.
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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 | 0.003 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.001 |
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".