Assessment of Anticipated Emotions in Moral Transgressions
Bibliographic record
Abstract
Abstract. This paper describes the reliability and validity of the assessment of anticipated emotions in the context of moral transgressions in a sample of 1,179 children aged 6–13 years (M = 9.1; SD = 1.8, 49.0% girls), with a special interest in the domain and developmental specificity of the instrument. To evaluate the concurrent and predictive validity, we also examined the relation between anticipated emotions and antisocial and prosocial tendencies and sympathy at two time points. The instrument consisted of six transgression scenarios covering three domains: unfairness (not winning fairly, not keeping word), omission of a prosocial duty (not sharing, not helping), and victimization (verbal bullying, relational bullying). Results show sufficient internal consistency and a one-factor structure of the anticipated emotions, indicating a lack of domain variability of the assessment of anticipated emotions. Additionally, emotions following hypothetical moral transgressions showed some developmental variability. Whereas no relation was found between anticipated emotions and antisocial tendencies, anticipated negative emotions following the moral transgressions were positively related to prosocial tendencies and sympathy. This provides preliminary evidence for the concurrent and predictive validity of the instrument.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".