Reasons for Forgiving: Individual Differences and Emotional Outcomes
Bibliographic record
Abstract
This research is part of a program to identify common forms of forgiveness and study the outcomes associated with different ways of forgiving. Two samples, one in Canada ( N = 274) and one in India ( N = 159), completed a third version of the Reasons for Forgiving Questionnaire (R4FQ), several measures of individual differences, as well as measures of affect and mood while imagining their injurer. Nine R4FQ subscales were derived: For the Relationship, To Feel Better, Based on Principle, Because Injurer Reformed, To Demonstrate Moral Superiority, Because Understood Injurer, For God, Because of Social Pressure, and For Pragmatic Reasons. These subscales were differentially related to religiosity, attachment security, trait anger, collectivism, and individualism. Positive emotional outcomes were associated with forgiving for the relationship, based on principle, because injurer reformed, and because understood injurer. In contrast, negative outcomes were associated with forgiving To Demonstrate Moral Superiority, Because of Social Pressure, and For Pragmatic Reasons.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".