Conceptualizing forgiveness: A review and path forward
Bibliographic record
Abstract
Summary Forgiveness is a valuable conflict management strategy that has numerous benefits in workplace settings (e.g., for employees, team dynamics, dyadic relationships, and organizations). However, important conceptual questions have emerged, especially as scholars have begun to examine forgiveness in the workplace. To better understand these issues, we conduct a critical review and analysis of the extant literature to identify key conceptual issues that are creating challenges for the study of forgiveness in organizational behavior. Building on these insights, we propose that conceptualizing forgiveness as a special case of emotion regulation can provide a strong conceptual and theoretical foundation that can address these challenges. Moreover, we outline how this approach can create exciting new research avenues that can enhance our theoretical understanding of forgiveness (e.g., distinguishing between the processes underlying forgiveness; identifying points of intervention to promote forgiveness; exploring the role of time in forgiveness; examining how context impacts forgiveness). We also identify how this approach can provide novel practical insights into how forgiveness can be facilitated and effectively managed in the workplace.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".