Smart payment systems, digital divide and transit inequity: a study of the Toronto Transit Commission's implementation of the Presto System
Bibliographic record
Abstract
Toronto is city divided. The city’s public transportation system is not an exception to this pattern. A move away from tokens, tickets, passes and cash and towards smart technology and modernization is excluding a large population of Torontonians who rely on public transportation but lack resources, face limited connectivity and rely on fare subsidy programs and traditional methods of fare payment. This paper aims to answer the question: How has the implementation of PRESTO on the Toronto Transit Commission furthered transit inequity in the City of Toronto? Through secondary data analysis and spatial analysis, this paper intends to explore the connection between the existing and worsening digital divide and the lack of access to physical PRESTO infrastructure outside of the downtown core, specifically in the inner suburbs and fringes of the city, areas where concentrations of low income and racial and ethnic minorities are higher. Key words: smart cards, transit fare payment, PRESTO, TTC, Toronto, transit equity, digital divide, digital literacy
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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 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".