Latent Profile Analysis of Moral Foundations: Emotional and Decisional Forgiveness Approaches to Models of Morality
Bibliographic record
Abstract
This study delves into the intricate relationship between morality and episodic forgiveness (i.e. emotional and decisional), guided by moral foundations theory. Survey data were collected from 927 English-speaking Canadians, aged 18-57, using the Moral Foundations Questionnaire, Decision to Forgive Scale, and Emotional Forgiveness Scale. Employing latent profile analysis, the research revealed three distinct moral foundation profiles-high moralists, individuators, and neutrals-each linked to different levels of decisional and emotional forgiveness. Further analysis using MANOVA and follow-up ANOVAs indicated that the high moralist group exhibited higher scores in both forgiveness dimensions compared to the individuator and neutral groups, whereas the individuator group reported higher emotional forgiveness than the neutrals. These findings illuminate the significance of moral development in forgiving and underscore the utility of moral profiling based on moral foundations theory in predicting episodic forgiveness.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 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".