Bibliographic record
Abstract
Increasing public transit ridership is a goal for most transit agencies and plays a central role in many recent regional transportation plans. Therefore, a comprehensive understanding of the determinants of mode choice and their effects over time is important. This study sought to understand how accessibility to employment by public transit changes over time, and how this accessibility explains changes in transit use. With the use of linear regression analysis, the authors explored the influence of job accessibility, transport infrastructure, and social disadvantage on transit mode share for three job categories in Toronto, Ontario, Canada, in 2 years, 1996 and 2006. New transit infrastructure did not necessarily attract more transit commuters but was found to affect commuting to different job categories differently. Also, new highway infrastructure hampered transit mode share, regardless of job type. The aggregate all-jobs model was found to dilute some differences between the transit mode choices of people commuting to different job categories. Finally, increases in accessibility by transit were found to augment transit mode share, while people in more socially disadvantaged areas were more likely to commute by transit in any job category. This study reveals findings that may be of interest to land use and transportation planners working toward boosting regional transit ridership, while also attaining social equity goals.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.007 | 0.001 |
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".