Reconsidering Forgiveness and Unforgiveness: A Call for a More Nuanced Understanding of Unforgiveness
Bibliographic record
Abstract
ABSTRACT This essay explores the complex nature of forgiveness and unforgiveness as responses to wrongdoing. Traditionally, forgiveness has been framed as a process that promotes psychological and relational well‐being, whereas unforgiveness is often viewed as harmful. Perhaps because of these tendencies, forgiveness researchers have documented many benefits associated with forgiveness but paid less attention to the possibility that there may be situations in which unforgiveness may be legitimate, appropriate, and perhaps even adaptive. We propose that the psychology literature could benefit from systematic investigation of unforgiveness that explores its complexity, potential moral dimensions, and the conditions under which it may serve as a valid and useful alternative to forgiving. By advocating for a more comprehensive and nuanced understanding of both forgiveness and unforgiveness, we seek not to diminish forgiveness or prior forgiveness research but, rather, to encourage a more balanced perspective on these constructs that may lead to more nuanced theories and interventions in both clinical and applied settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.068 |
| Scholarly communication | 0.013 | 0.023 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.002 | 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".