Distinctions between experiences of anger and sadness in children's and adolescents' narrative accounts of peer injury
Bibliographic record
Abstract
Abstract Children's varied emotions following peer injury may reflect distinct ways of understanding and coping with such events. This study examined how children's references to anger and sadness in their accounts of peer injury were differentially related to narrative descriptions of their motivations, interpretations, evaluations, and behavioral responses, as well as the relationships in which harm occurred. We also explored how these associations between emotions and other narrative elements varied with age. The study was based on a corpus of 275 transcripts of oral narratives recounted by equal numbers of boys and girls across three age groups: 7, 11, and 16 years. In line with functionalist theories, anger was uniquely linked to maximizing attributions, indignation, and aggression, after accounting for age and gender. Sadness was related to harm in close relationships and relational goals, underlining the value placed on relationships with the offender, as well as a sense of powerlessness and confusion. Some associations between emotions and other narrative elements varied with age, suggesting that children's experiences of anger and sadness became increasingly agentic and relationally oriented. Findings suggest how narrative constructions of meaning about peer injury may serve as contexts for reflecting on how anger and sadness emerge from and are resolved through interpersonal relationships.
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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 | 0.008 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".