Understanding the Perpetrator’s Experience: Shame, Guilt, and Forgiveness
Bibliographic record
Abstract
Research on transgressions has focused almost exclusively on the experience of the victim.Extrapolating from the attributional theory of motivation and emotion, this research aimed to gain a better understanding of how individuals make sense of a perpetrator's experience following transgressions.In three studies (combined N = 288; 73.3% female; Mage = 21.3)undergraduate students read hypothetical transgressions and assessed perceived likelihood of shame, guilt, forgiveness seeking, and self-forgiveness from the actor in each scenario.Results suggest that people sometimes do make a distinction between shame and guilt and that causal attributions and whether victims were involved in the transgression may aid people in making this distinction.Results also suggest that causal attributions, the presence of other victims, and perceived emotions may influence perceptions about forgiveness.These findings may allow for a more in-depth understanding of the psychology of transgressions and may have implications for law and conflict resolution.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".