The Multidimensional Forgiveness Inventory: A Model for the Assessment of Incongruent and Incomplete Forgiveness
Bibliographic record
Abstract
Building on existing models, we theorized a dimensional model which quantifies forgiveness in terms of incongruence among forgiveness aspects. In the present research we aimed to draw on attitudinal theory to validate a measure of forgiveness which assessed cognition, behavior, and affect. Our goal was to develop a measure of forgiveness which captures incongruence between domains. To do this, we examined the validity and reliability of the Multi-dimensional Forgiveness Inventory (MDFI), which assess forgiveness-relevant behavior, cognition, and affect. In Study 1 we assessed convergent/divergent validity and assessed predictive associations among dimensions and theoretically relevant constructs. In Study 2 we assigned participants to rate their forgiveness for transgressions (small or large), at two different time points. This allowed us to assess temporal stability of dimensions across similar and dissimilar transgressions. In Study 3 we replicated research on embodied remorse using the MDFI to assess forgiveness. We found that for transgressors demonstrating embodied remorse (i.e., kneeling), participants were more willing to communicate forgiveness, but we found no difference in cognitive or affective forgiveness (in line with past research). The present research provides a novel model and measure to assess incongruent forgiveness.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".