The impact of affect, resilience, and empathy in forgiving friends
Bibliographic record
Abstract
The current study sought to observe how positive personality traits, namely positive affect, empathy, and resilience, may affect an individual's willingness to forgive a friend following a transgression. This study was conducted using 107 students from the University of Massachusetts Dartmouth in USA and 137 students from the University of Calgary in Canada. Participants completed surveys over two sessions, in which they were asked to recall a recent transgression that was committed by a same-sex friend. Part 1 consisted of surveys measuring forgiveness on a dispositional level, while Part 2 consisted of surveys that asked the participant about the specific transgression and about their relationship with their friend. The current study used a selection of measures from a larger data set. The study also considered two different measures of forgiveness; both as a personality trait (trait forgiveness) and as a measure of the participant's feelings towards their transgressor at the time of taking the survey (state forgiveness). The results indicated that state and trait forgiveness were associated with different sets of variables. Trait forgiveness was associated with personality-level variables including resilience and empathy, whereas state forgiveness was related to relationship-level variables including offense severity, perceived apology, and length of relationship. Positive affect was the only variable that was positively associated with both state and trait forgiveness. Results also pointed to some interesting sex differences; men reported showing more benevolence, while women showed more avoidance towards their transgressor. The implications of the findings are further discussed.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".