PA 08-4-2305 School children practices as pedestrians in karachi, pakistan
Bibliographic record
Abstract
Pedestrian road traffic crashes are responsible for a substantial number of injuries and deaths in Pakistan. There is a need to better understand the situations faced by pedestrians especially children. The objective of this study was to develop and pilot observation tool for pedestrian’s behavior and road practices of school children in Karachi. The survey was conducted from March to June 2013. Initially 175 schools were approached out of which (n=107, 61.1%) agreed to participate. The observations were made on school children as pedestrians (coming to and going from school) by trained data collectors. The observations were made during morning (starting time of school) and afternoon (off time of school). Three hundred and forty-four pedestrian observations were made in 107 schools. Of the 107 schools, 50.47% were private (n=54), 44.86% were public (n=48), and the rest were non-governmental organization run (n=5, 4.67%) schools. Most of the schools (n=227, 66.6%) lacked a zebra crossing. None of the observed children used zebra crossing, when present. Only a quarter of the children looked right and left while crossing the road (n=85, 24.7%). Almost one third of the children had their back towards oncoming traffic while walking on road (n=109, 31.7%). About 10.5% (n=36) children ran to cross the road. About 36.3% (n=125) children did not look out for traffic before stepping on to the road. This was the first time that safety behaviors of children in school as pedestrians were measured in Pakistan. There is need for improved safety for child pedestrians while promoting the health and environmental benefits of walking.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".