Die Entwicklung moralischer Emotionen bei Kindergartenkindern
Bibliographic record
Abstract
We investigated the development of young children's moral emotions, i.e., sympathy and guilt, within a multi-informant longitudinal research design. One-hundred and seventy-five 6-year-old Swiss children participated at the first assessment (T1; re-assessed after one year; T2). Mothers and teachers participated as well. Sympathy was measured with self- and other reports and within children's play narratives. Guilt feelings were assessed by emotion attributions to protagonists and self-as-protagonist following different hypothetical moral transgressions. Mother-rated sympathy at T2 was positively related to guilt feelings at T2, whereas no relations between sympathy and guilt were found at T1. Sympathy ratings within informant were stable over a year. Guilt feelings to protagonists and self-as-protagonist were stable over a year as well. Guilt feelings predicted subsequent guilt feelings, whereas earlier sympathy did not predict guilt feelings. The results are discussed in regard to the differential development of moral emotions.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".