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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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 teacher head, 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".