Pathways to Transit Equity in the Suburbs: A Study of Brampton, Ontario
Bibliographic record
Abstract
Public transit has been identified by scholars, urbanists, and activists as foundational to developing equitable cities. However, the acute impacts brought on by COVID-19 clearly exposed public transit’s current vulnerability to crisis. This research takes an equity lens to critically probe the inequities present within Brampton’s auto-centric transportation network. Public transit occupies a central node in public life, and its value transcends simply transportation. By rejecting the neoliberalization of public transit and the ensuring the provision of substantial public funding, public transit’s role in the city and in the everyday lives of urban residents can be reimagined. Equitable public transit provides riders with fair and just access to mobility and the ability to more fully participate in collective life, irrespective of gender, race, class, and ability. My research reveals the barriers to transit access experienced by Brampton’s transit riders, demonstrates how COVID-19 has intensified such inequities, and proposes a series of measures to begin to address the barriers unevenly faced by transit-dependent residents. This paper will analyze Brampton and how it interacts with the surrounding urban region to examine if the city’s transportation infrastructure and transit service standards produce and contribute to inequitable outcomes for transit-dependent residents. The express intent of public transit should be to provide an equitable baseline public service to all, fare free. \nHowever, under neoliberal governance models, publicly owned and operated transit is subjected to market factors and thus the risk of deterioration and privatization.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.018 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".