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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".