An empirical test of forgiveness motives' effects on employees' health and well-being.
Bibliographic record
Abstract
Two critical-incident studies were conducted to determine what motivates employees to forgive (or reconcile) with coworkers who offend them. Data from the first study's exploratory factor analysis revealed five types of motives for forgiveness: apology, moral, religious, relationship, and lack of alternatives. Data from the second study on a different sample confirmed the five-factor structure, and structural equation modeling demonstrated differential relationships between the five motives and the outcome variables, stress and health. Individuals who claimed to have forgiven because they believed they had no other alternatives, or who forgave because they believed a higher power (religious) required it, were more likely to report greater stress and poorer health. Positive outcomes of forgiveness were discovered for those employees who forgave because they believed it was the right (moral) thing to do. Those who forgave for moral reasons reported less stress than those who forgave because they believed they had no other choice or because a higher power demanded it. Forgiving for relationship and apology reasons was not significantly related to either stress or general health. Future research directions are discussed.
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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 | 0.011 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".