The Virtue in Vice: Moral Judgments of Prosocially Motivated Transgressions
Bibliographic record
Abstract
While prosociality and morality often align, prosocial motives can drive transgressive behaviour, complicating the moral judgments of both the transgressor and the act. In this dissertation, I examined how observers evaluate prosocially motivated transgressors and transgressions. I began by identifying the prevalence of prosocially motivated transgressions (Study 1). Then I used tightly controlled hypothetical transgressions (Study 2) and ecologically valid criminal court cases (Study 3) to investigate the impact of a transgressor’s prosocial motives on observers’ moral evaluations and identify under what conditions these motives had a stronger or weaker impact (Studies 4-6). Generally, prosocially motivated transgressors evoked less harsh responses from observers, including lower conviction rates and marginally more lenient sentences, compared to transgressors motivated by concern for their own welfare, other social motives (i.e., coercion), or whose motives were not stated. This effect was moderated by the relationship between the transgressor and the person they were trying to help (a spouse versus a stranger) and the severity of the transgression (Study 4). Critically, greater leniency toward prosocially motivated transgressions rested upon two conditions—that the beneficiary’s need was high (Study 5) and that other ethical alternatives had been exhausted (Study 6). In the absence of either of these conditions, transgressions motivated by prosocial concerns were judged equally harshly as those motivated by self-interested concerns. However, when both conditions were present, transgressors with prosocial motives crossed the threshold to being evaluated as moral, though the act itself was still not permissible (Study 7). Together, these findings suggest that moral evaluations of prosocial transgressions are social, adaptive, and context sensitive.
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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 | 0.004 | 0.033 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".