School Travel Inequity between Students from Public and Private Schools in the City of Shenzhen, China
Bibliographic record
Abstract
Understanding school travel inequities and promoting active travel policies more effectively is an increasingly important issue in the international transport policy agenda. Using the dataset of the 2014 Shenzhen primary and secondary school travel survey, this study empirically revealed the permanent residence permit (hukou) system in the context of China shapes the evident inequities between students from public schools and private schools. Students without a legitimated hukou to local areas suffer from more constraints, longer distances, and more time to access private schools which are excluded from the public sponsorship and have disadvantages in geographical locations. Applying the ordered logistic model, this study specifically investigated the influential factors of school commuting travel mode. Household vehicle ownership and travel features (i.e., chauffeuring and home-school distance) have a much more significant role in school travel mode decisions, which largely surpassed the role individual demographic attributes and the school surrounding built environment play. The implications of this study shed light on making more specific strategies for private schools to mitigate mobility inequity imposed on disadvantaged students.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".