Towards a equitable approach to tackling the fare evasion problem: a scoping literature review and case study analysis
Bibliographic record
Abstract
The widespread adoption of “proof-of-payment” ticketing systems by public transit corporations (PTCs) has renewed interest in the topic of fare evasion. Although this system has many benefits, it has also been associated with higher rates of actual or perceived fare evasion. As such, many PTCs including the TTC in Toronto have also simultaneously invested in heightened measures to curb fare evasion. These measures, however, have usually taken the form of increased fines and policing, which have the potential to further disadvantage marginalized populations. A scoping literature review and case study analysis have been employed to determine whether there is existing evidence that can make a case for the need for a more equitable solution to this problem, and to determine what alternative measures have been effective. Although there is much evidence to support the need for a more equitable approach, research into alternative measures is emerging and therefore somewhat inconclusive.
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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 | 0.147 | 0.235 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.074 | 0.063 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.018 | 0.019 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".