Using Peer Communicated Norms About Safety to Reduce Injury-Risk Behaviors by Children
Bibliographic record
Abstract
Objective: This study examined whether exposure to a safety norm could counteract the increase in risk taking children show when in an elevated positive mood state. Methods: Risk taking (intentions, behaviors) was measured in a neutral and positive (induced experimentally) mood state. Before completing the tasks in a positive mood, 120 children 7-10 years were exposed to either a safety norm or a control audio. Results: The control audio had no effect: children showed an increase in risk taking and intentions when in a positive mood compared with a neutral mood, replicating past research. In contrast, exposure to the safety norm counteracted this effect: children showed a decrease in risk taking and intentions when in a positive mood compared with a neutral mood. Conclusion: Manipulating children's exposure to social norms can be an effective strategy for reducing injury-risk behaviors even when they are in an elevated positive mood state.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".