Judging extreme forgivers: How victims are perceived when they forgive the unforgivable
Bibliographic record
Abstract
When one individual commits a transgression or aggressive act against another, third parties often have expectations about how the victim should respond, even when they do not have any personal involvement in the event. When their justice expectations are violated, such as when a victim forgives the offender for an act that third parties deem too heinous to forgive, third parties may react in a way that is critical of the victim. This research examines how third-party observers react when victims forgive seemingly ‘unforgivable’ offences. Study 1, a scenario-based experiment, showed that although third parties were not directly critical of a forgiving victim, they did not agree with the decision to forgive. Study 2 replicated these findings and explored in more depth third parties’ justice-related feelings about the transgression and the victim, using both quantitative and qualitative data. Results suggest that although third parties are reluctant to directly criticize ‘extreme’ forgivers, they are not supportive of their decision to forgive. This could have implications for victims, who may interpret this disagreement with their choice as a lack of support.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".