Bibliographic record
Abstract
In recent years, public transit policy has often focused on chasing ‘choice’ riders, or those who have mode alternatives, while taking for granted ‘captive’ riders, or also referred to as transit dependents. This paper argues for a need to re-centre attention towards ‘captive’ riders through equity and sustainability perspectives, and to question the use of the term ‘captive’, as it alludes to marginalization. We conduct this research by examining the transit experiences of a sample of young captive riders in Don Valley Village and Crescent Town, two high-rise suburban neighbourhoods in the City of Toronto. Semi-structured interviews are used to gain insight into participants’ travel patterns and the challenges associated with public transit use. Participants accrue different types of costs with their experiences (i.e., time, money, safety, and comfort), which do limit their ability to participate in public life. The study is situated in the broader context of transit equity, which point to the need for service quality improvements for ‘captive’ riders. This study also shows why assessments of young captive riders’ experiences is essential for planning. Contrary to how captive riders are perceived, service quality issues prompted some of the study participants to switch to driving, which further questions the categorization of ‘choice’ and ‘captive’. Transit agencies are urged to consider further how to improve transit quality for ‘captive’ riders to contribute to equity but also to maintain transit loyalty among younger transit riders as their circumstances change.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".