Self‐forgiveness: The Good, the Bad, and the Ugly
Bibliographic record
Abstract
Abstract Traditionally, self‐forgiveness has been framed as a process that helps facilitate psychological as well as physiological well‐being following wrongdoing. In the present paper, we outline the limits and boundaries of this presupposition. Specifically, we outline contexts in which self‐forgiveness might yield negative consequence that include, among other things, a continuation of the wrongful behavior. First, we provide evidence that self‐forgiveness for ongoing, wrongful behavior (e.g., smoking) alleviates negative feelings associated with acknowledged wrongs committed by the self, which does little to motivate behavioral change. We then discuss the complication that is pseudo‐self‐forgiveness – a situation in which people shift some responsible away from the self for wrongs committed by the self. This outward shift in responsibility lets the self “off the hook”, which increases the likelihood that the wrongful behavior will continue. Drawing on these discussions, a path model for behavioral change that places self‐forgiveness at its core is offered. Although we present some pessimism regarding the outcome of the self‐forgiveness process, this paper points to situations and attributions that maximize its positive effects.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".