Equity-related Benefits of Public Transit Expansion: An Exploration of Post-secondary Student Travel Behavior Between 2015 and 2019 in the Toronto Region
Bibliographic record
Abstract
This study compared transit equality before and after the Toronto-York Spadina Extension. I examine the equal distribution of subway ridership increase by socioeconomic status from 2015 to 2019. StudentMoveTO survey (Mitra et al, 2020) and Ontario margonalization Index are the key data sources ( Matheson et al., 2022). First, I look at public transit use at six universities in Toronto region. Second, I study the socio-demographic characteristics of subway riders from 2015 to 2019 on Keele campus. Using hot spot analysis, Generalized linear regression, and geographically weighted regression, I examine student subway use and if growth is correlated with socially excluded neighbourhoods. The study indicated an increase in subway use among post-secondary students to/from six campuses, especially among female, trans, and non-binary students and students with less than $30K family income who use subway to commute to/from York University's Keele campus. The data show that the relationship between student subway use and marginalised neighbourhoods varies by census tract. Most areas don't see a strong positive association between marginalisation index and the growth in using subway among the student.
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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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".