Examining the impacts of school bus travel on students' academic performance in two major cities
Bibliographic record
Abstract
Abstract School buses are a prevalent mode of school travel that may negatively affect students’ academic performance due to numerous factors, including longer commutes. These extended travel times can introduce or exacerbate mental and physical stressors, including air pollution exposure and bullying, while reducing opportunities for health‐promoting activities like physical activity and sleep. While a few researchers have explored the effects of school bus transportation on academic achievement, the research is limited. In this study, we used data from two major Canadian cities, Toronto and Ottawa, to investigate the relationship between the proportion of students commuting by bus to and from school and the percentage meeting standards on standardized tests. We employed beta regression models to analyze performance in reading, writing, math, and literacy across Grades 3, 6, 9, and 10 while controlling for family and income variables that may influence school travel mode and student achievement. Our findings indicate a significant inverse correlation between school bus transportation and academic achievement for Grade 10 literacy. Longer commutes appear to impair academic performance, given that Grade 10 students presumably spend more time on the bus than younger students. Notably, the negative effects on literacy outcomes are greater than on math.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".