Urban cycling equity : an analysis of equity based approaches in public bike sharing program development in Toronto
Bibliographic record
Abstract
The prominence of bike sharing programs as an alternative mode of green transportation has captured the attention and stimulated renewed enthusiasm among urban cyclists, municipalities, and urban planners alike. However, researchers and critics question the distributive effects of these services on marginalized communities. When examined closer, studies demonstrate that bike share users have on average, a high education status, work full time, and have high incomes. Moreover, older adults, women, and low-income communities remain marginally represented in bike share user demographics throughout major North American cities. In recognition of these observations, this Major Research Paper (MRP) explores how these equity considerations are relevant to Bike Share Toronto (BST), the City of Toronto’s publicly-owned bike share program. This analysis uses mixed method research including spatial analysis of the existing BST service area, an analysis of BST Equity Survey results, and expert interviews with representatives with key institutional perspectives on bike share equity. Finally, this research highlights key considerations relevant for the development of a bike share equity intervention by BST. Key Words: Active transportation, Bike Share Toronto, transportation equity, bike share equity programs
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".