The reduction of psychological aggression across varied interpersonal contexts through repentance and forgiveness
Bibliographic record
Abstract
Research on the resolution of interpersonal conflict has shown that forgiveness is important in reducing aggression and promoting prosocial interactions following a transgression. Although the benefits of forgiveness have been demonstrated in a variety of relationship contexts, a single theoretical model has not been tested across these different contexts. In this study, we employed an attributional framework to examine the relationship between attributions of responsibility for a transgression, repentance, emotions, forgiveness, and psychological aggression toward three different categories of transgressor: a coworker, a friend, and a romantic partner. One hundred and seven participants were asked to describe a recent transgression with a coworker, a friend, and a romantic partner. In each case, responsibility for the event, the degree to which the transgressor apologized, anger, sympathy, forgiveness, and subsequent psychological aggression toward the transgressor were measured. A basic model of aggression reduction, whereby repentance facilitates forgiveness and reduces psychological aggression, was reliable in each category of transgressor. A comparison of the models showed minor differences in how individuals respond to transgressors. Although coworkers apologized less, they were just as likely to be forgiven as romantic partners and friends. In addition, participants were least likely to respond with psychological aggression when a friend transgressed against them. This research provides a theoretical framework within which to study forgiveness and aggression across a variety of contexts. Aggr. Behav. 32:1–12, 2006. © 2006 Wiley-Liss, Inc.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".