Bibliographic record
Abstract
In the present research we extended previous studies examining moral emotion expectancies in childhood to investigate the relationship between moral emotion expectancies and moral behavior in adolescence. A secondary goal was to explore the relationships among moral emotion expectancies, the moral self, and moral action. Two hundred and thirty-five adolescents in grades 7, 9, 11, and first year university completed a structured interview assessing moral emotion expectancies in various situations in which a moral norm is either regarded or disregarded. Participants distributed up to 10 plastic chips on nine emotional expressions to indicate how they expected to feel in each moral situation and also provided an overall emotion rating for each scenario that averaged across all the specific emotions they anticipated. A written questionnaire measured self-reported prosocial and antisocial behavior by asking participants how often they engaged in a list of activities in the past year. The questionnaire also included a measure of self-centrality of moral values to the individuals’ identity. Self-evaluative moral emotion expectancies were shown to have associations with antisocial and prosocial behavior, but it was the overall emotion ratings that were most closely associated with behavior. Thus, moral emotions do not appear to stand out against other, more basic emotions when predicting moral action. These overall ratings were associated with self-reported levels of antisocial behavior while moral self scores were better predictors of self-reported prosocial behavior. Additionally, the relationship between emotion expectancies and antisocial behavior was also found to be moderated by age, with emotion expectancies becoming more predictive of self-reported antisocial action with age.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".