Traveling Fair: Targeting Equitable Transit by Understanding Job Location, Sectorial Concentration, and Transit Use Among Low-Wage Workers
Bibliographic record
Abstract
Low-wage workers have a pressing need for adequate and affordable transportation services. However, the growing polycentricity of North American metropolises means transit providers face the difficult task of serving ever more dispersed employment centers. Deciding where limited project resources would provide the most benefit for disadvantaged populations should be a persistent concern for transit planners and elected officials. The purpose of this research is to determine where low-wage employment zones are, where different types of low-wage jobs concentrate, and determine if job type and location have an effect on transit ridership for low-wage workers. The authors use a previously proposed method to identify low-wage employment zones in the Greater Toronto Hamilton Area, Canada and measure job type concentration using a gravity approach. They then test to see if job type concentration and employment centres relate to ridership, while controlling for other factors that influence mode share. Their results indicate significant differences in transit-use for different occupations. These results can help guide more transit investment and research by tackling specific occupation’s travel needs.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".