Travelling fair: Low-wage commuting in the Greater Toronto and Hamilton Area
Bibliographic record
Abstract
In order for transportation investment to be equitable those with little disposable income should receive an equitable share of transit service. Low-wage workers already experiencing limited choices in residential location and struggling to meet daily necessities require access to affordable and efficient transportation. The purpose of this project is to discover when low-wage workers and higher-wage workers in the Greater Toronto and Hamilton Area use transit and how variations in worker needs and transit service impact ridership. We then investigate how occupational sector relates to low-wage ridership and use these findings to propose a novel method for targeting policies, infrastructure, and marketing to areas and sectors of low-wage employment that require help. We find that low-wage workers use transit less than their higherwage counterparts and that factors that increase transit use among higher-wage workers decrease transit use among low-wage workers. We also find that certain sectors of low-wage employment have a negative pull on transit use. By discovering areas of low-wage employment concentration in the GTHA, we can pinpoint areas where investment would be most beneficial for this vulnerable population. For practitioners and researchers, the findings and methods of this project can be replicated in other jurisdictions. In particular, we demonstrate that focusing research and interventions on employment locations may be a novel way to efficiently target transit investment.
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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 | 0.003 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".