Moral self and moral emotion expectancies as predictors of anti- and prosocial behaviour in adolescence: A case for mediation?
Bibliographic record
Abstract
Previous research has linked (im)moral behaviour with both moral emotion expectancies and the self-importance of moral values, indicating that these two factors influence moral decision making and action. Disentangling the relationship between moral emotion expectancies and self-importance of moral values as predictors of adolescents' anti- and prosocial behaviour was the primary goal of this research. Two hundred five participants (mean age = 14.83 years) completed a semi-structured interview assessing moral emotion expectancies in hypothetical situations and a written questionnaire measuring self-reported prosocial and antisocial behaviour and the self-importance of moral values. Moral emotion expectancies were found to mediate the relationship between the self-importance of moral values and self-reported levels of antisocial behaviour. When predicting levels of prosocial engagement, however, scores of moral value self-importance were the primary variable associated with prosocial behaviour whereas moral emotion expectancies were not involved in this relationship. In addition, a moderating effect of age was found when predicting antisocial behaviour by moral emotion expectancies. Overall, the study confirms and significantly extends previous research on the relationship between adolescents' moral self, moral emotion expectancies and anti- versus prosocial behaviour.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".