Forgiveness and Collective Guilt Assignment to Historical Perpetrator Groups Depend on Level of Social Category Inclusiveness.
Bibliographic record
Abstract
The authors examined how categorization influences victimized group members' responses to contemporary members of a historical perpetrator group. Specifically, the authors tested whether increasing category inclusiveness--from the intergroup level to the maximally inclusive human level--leads to greater forgiveness of a historical perpetrator group and decreased collective guilt assignment for its harmdoing. Among Jewish North Americans (Experiments 1, 2, and 4) and Native Canadians (Experiment 3) human-level categorization resulted in more positive responses toward Germans and White Canadians, respectively, by decreasing the uniqueness of their past harmful actions toward the in-group. Increasing the inclusiveness of categorization led to greater forgiveness and lessened expectations that former out-group members should experience collective guilt compared with when categorization was at the intergroup level. Discussion focuses on obstacles that are likely to be encountered on the road to reconciliation between groups that have a history of conflictual relations.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".