Understanding Children's Injury-risk Behaviors: The Independent Contributions of Cognitions and Emotions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: Unintentional injuries are a leading threat to the health of elementary-school children, with many injuries happening when children are left to make their own decisions about risk taking during play. The present study sought to identify determinants of children's physical taking. METHODS: An ecologically valid task that posed some threat of injury was used (i.e., highest height of a balance beam they would walk across). Ratings of cognitions (extent of danger, perceived vulnerability for personal injury, potential severity of injury) and emotional reactions (fear, excitement) were taken when on the beam, just before the children walked across. RESULTS: Regression analysis, controlling for age and sex, revealed that risk taking was predicted from ratings of danger, fear, and excitement. CONCLUSIONS: Both cognitive and emotional factors independently contribute to predict children's physical risk taking. Theoretical and practical implications of these findings are discussed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it