Coping with unfair events constructively or destructively: The effects of overall justice and self–other orientation.
Bibliographic record
Abstract
Drawing on fairness heuristic theory (Lind, 2001, 2002), it was predicted that how employees cope with an unfair event-whether they are more or less forgiving, and whether they are more or less vengeful-will depend jointly on (a) their perceptions of overall organizational justice and (b) the degree to which they focus on their own interests or on the interests of others. Data were collected in a 2-part field survey of 153 employees who reported their responses to a recent unfair event. Hierarchical regression analyses (controlling possible 3rd variable explanations) revealed the 2 predicted 2-way interactions. Perceptions of overall organizational justice (a) facilitated forgiveness among those with strong other-orientation, and (b) suppressed revenge among those with strong self-concern. Together, the data suggest that perceiving one's organization as a fair entity can shape proximal responses to unfair events, simultaneously facilitating constructive responses in some employees, and suppressing destructive responses in other employees. Theoretically, the findings are consistent with the idea that overall justice fulfills psychological needs that are differentially relevant to employees as a function of their chronic attention to others or to themselves, which in turn enables them to cope with unfair events more beneficially. The data have implications for the study of workplace forgiveness and revenge, as well as more broadly for the literatures on organizational justice and workplace mistreatment.
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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 | 0.003 | 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 | 0.001 |
| 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".