Between Law and Conscience: The Role of Legality in Moral Decision-Making
Bibliographic record
Abstract
Does the legal status of an action shape how morally wrong people perceive it to be? Across three experiments (N = 1,226), we find that participants judged actions to be more morally wrong when labelled as “illegal” as opposed to “not illegal.” Furthermore, we demonstrate the robustness of this effect, revealing the impact of act legality on moral evaluations across manipulations of agent intentionality and type of law-making process. Notably, the influence of act legality was not restricted to judgments of actions, but also guided perceptions of others’ moral character. Experiments 1 and 2 revealed individual differences in the extent to which people’s moral judgments were shaped by an act’s legal status, with act legality being most influential for participants viewing respect for authority as a moral good. Based on these findings, we forward an account in which individuals use legal judgments as heuristic cues when making moral evaluations.
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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 | 0.007 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".