Bibliographic record
Abstract
The study explored the relationship between children's response to risk in the form of a simulated bicycle accident, and several variables hypothesized to be related to risk appraisal. Specifically, the relationship between reported levels of sensation seeking, competitiveness, impulsivity, fear of failure, thrill and adventure seeking, experience seeking, boredom susceptibility, self-sufficiency, conformity, previous injury behaviour, gender, bicycle confidence, galvanic skin response and cycling behaviour in a simulated setting were examined in 60 Grade 2 and Grade 6 children. The following hypotheses were made regarding the association between risky behaviour in the variables listed above; Based on previous research it was hypothesized that Sensation Seeking, Competitiveness, Thrill and Adventure Seeking, Self Sufficiency, Experience Seeking, Boredom Susceptibility, Previous Injury Behaviour, and Bicycle Self-Confidence would all be positively correlated with risky behaviour (faster pedaling and late braking). Conversely, Conformity, Fear of Failure (test anxiety), and Galvanic Skin Response will be negatively correlated with risky behaviour. As well, males were predicted to show greater risk taking behaviour, as were Grade 6 children in general. Results indicated that Grade 2 subjects displayed riskier cycling behaviour in the simulator by pedaling faster than the stimulus film required them to, and by braking later in response to the hazard presentations. The questionnaire and GSR data did not predict risk taking behaviour in the simulator. The implications of these findings from developmental standpoint, and as a guide to further research are discussed.
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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 | 0.000 | 0.004 |
| 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 | 0.000 |
| 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".