Moral Paragons, but Crummy Friends: The Case of Snitching
Bibliographic record
Abstract
Loyalty to friends is an important moral value, but does that mean snitching on friends is immoral? Across four pre-registered studies, we examine how loyalty obligations impact people’s moral evaluations of snitching (i.e., turning in others who commit transgressions to relevant authorities). In vignette and incentivized partner choice studies, we find that witnesses who snitch (vs. do not snitch) are seen as more moral and as better leaders (Studies 1–4), regardless of whether they snitch on a friend or an acquaintance (Studies 1–2). Our experiments also demonstrate that snitches receive less moral credit when snitching on non-moral (vs. moral) transgressions (Study 2), and when snitching aligns with a self-interested motive (Study 3). We demonstrate that although snitching is often seen as morally right, turning in transgressors entails important reputational tradeoffs: Snitching makes one appear disloyal and a bad potential friend, but boosts perceptions of morality and leadership potential.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".