Children's Self-reported Emotions and Emotional Facial Expressions Following Moral Transgressions
Bibliographic record
Abstract
This study examined self-reported emotions and emotional facial expressions following moral transgressions using an ethnically diverse sample of 242 4-, 8-, and 12-year-old children. Self-reported emotions were examined in response to three transgression contexts: an intentional harm, an instance of social exclusion, and an omission of a prosocial duty. Children’s emotional expressions of sadness, happiness, anger, fear and disgust were analyzed immediately after being asked how they would feel if they had committed one of the described transgressions. Emotional expressions were scored using automated emotion recognition software. Four-year-olds reported significantly more happiness as compared to 8- and 12-year-olds. In addition, self-reports of sadness decreased between 8- and 12-year-olds, while self-reported guilt increased between these age groups. Furthermore, 4- and 8-year-olds demonstrated higher levels of facially expressed happiness than 12-year-olds. These findings highlight the role of automatic affective and controlled cognitive processes in the development of children’s emotions following moral transgressions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".