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Record W4416608075 · doi:10.1007/978-3-031-99812-6_4

Governing Public Transit in Canada

2025· book-chapter· en· W4416608075 on OpenAlexaffabout
Martin Horák

Bibliographic record

VenueLocal and urban governance · 2025
Typebook-chapter
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsWestern University
Fundersnot available
KeywordsPublic transportTransit (satellite)Corporate governanceLegislationGovernment (linguistics)RevenueTransit systemDelegate

Abstract

fetched live from OpenAlex

Abstract This chapter provides a broad overview of the governance of public transit systems in Canada. Transit plays a relatively minor role in the overall Canadian transportation system, due to a combination of geographical and policy factors. Nonetheless, public transit is an important mode of transport in large Canadian cities. In legal terms, public transit is a responsibility of Canada’s provinces, but all provinces except for one in turn delegate this responsibility to municipal governments. This jurisdictional decentralization, together with the lack of general-purpose transit legislation in most provinces, means that the structure and governance of transit systems varies widely across cities. Transit operations depend heavily on fare box revenues, and the steep decline in ridership during the Covid-19 pandemic has produced a lingering operating revenue crisis. The capital funding situation is more positive, since all three levels of government have recently invested significant funds in expanding urban transit infrastructure. However, the vertical and horizontal fragmentation of transit governance in some city-regions has politicized, complicated and delayed the construction of new high-capacity transit lines. The chapter examines these issues and challenges with a specific focus on the three largest city-regions in Canada: Vancouver, Montreal and Toronto.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.906
Threshold uncertainty score0.949

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.011
GPT teacher head0.206
Teacher spread0.195 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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