An Examination of Time-Use and Transportation Barriers to On-Campus Participation of University Students
Bibliographic record
Abstract
Success in postsecondary education is related to the amount of time spent on campus. The more often students attend class and access on-campus learning resources, the better their grades and the lower their dropout rates. Despite the importance of on-campus participation in student outcomes, some students living in large cities face tremendous transportation and time-use barriers that make it difficult to spend more time on campus. Accordingly, the objective of our project is to examine the mobility factors that prevent students from attending their campuses in the Greater Toronto Area (GTA). Specifically, we examine student disparities in barriers to participate based on where they live, their mobility options, as well as the time constraints of their daily activity patterns (e.g. part time work). Data for our project is drawn from a 1-day travel survey of students across seven university campuses in the GTA. This is augmented with computationally derived transport accessibility factors. Multivariate logistic regression models are then employed to uncover the mobility-related determinants for a) if students feel commuting discourages them from travelling to campus; b) if students pick courses based on their commute; c) if commuting discourages students from participating in university organized activities; and d) how many days per week a student visits campus. The results of these models fuel a discussion of how to limit mobility-related barriers to postsecondary student participation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".