Using a virtual environment to study child pedestrian behaviours: a comparison of parents’ expectations and children's street crossing behaviour
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to compare parents' expectations for their children crossing streets with children's actual crossing behaviours and determine how accurately parents judge their own children's pedestrian behaviours to be. METHOD: Using a fully immersive virtual reality system interfaced with a 3D movement measurement system, younger (7-9 years) and older (10-12 years) children's crossing behaviours were assessed. The parent viewed the same traffic conditions and indicated if their child would cross and how successful she/he expected the child would be when doing so. RESULTS: Comparing children's performance with what their parents expected they would do revealed that parents significantly overestimated the inter-vehicle gap threshold of their children, erroneously assuming that children would show safer pedestrian behaviours and select larger inter-vehicle gaps to cross into than they actually did; there were no effects of child age or sex. Child and parent scores were not correlated and a logistic regression indicated these were independent of one another. CONCLUSIONS: Parents were not accurate in estimating the traffic conditions under which their children would try and cross the street. If parents are not adequately supervising when children cross streets, they may be placing their children at risk of pedestrian injury because they are assuming their children will select larger (safer) inter-vehicle gaps when crossing than children actually do.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".