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Record W3182867392 · doi:10.37417/adm/14-2020_09

Building rapid transit in Canada: the problem of governance

2021· article· en· W3182867392 on OpenAlexafffundabout
Martin Horák

Bibliographic record

VenueAnuario de Derecho Municipal · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Planning and Governance
Canadian institutionsWestern University
FundersSocial Sciences and Humanities Research Council of CanadaEuropean Commission
KeywordsTransit (satellite)Metropolitan areaBusinessBoomGovernment (linguistics)Corporate governanceFinancePublic transportEconomic growthTransport engineeringGeographyEconomicsEngineering

Abstract

fetched live from OpenAlex

Canadian cities have seen a boom in the construction of rapid transit infrastructure in recent years, fueled by the rise of financial support for transit from the federal government and the provinces. However, the extent to which individual cities have been able to productively harness this new financial support varies greatly. This study compares the recent development of rapid transit infrastructure in two of Canada’s largest metropolitan areas, Toronto and Vancouver. It finds that while both cities have recently developed regional transportation authorities to manage large transit investments, in Toronto the development of rapid transit has been highly contentious, marked by frequent changes in plan and the repeated cancellation and deferral of transit projects, while in Vancouver, the development of rapid transit has been much more consensual and orderly. The study introduces an analytical framework that interprets these different outcomes as the result of dissimilar institutional environments in the two cities, which vary in the extent to which they insulate long-range planning and decision-making from efforts by politicians to harness rapid transit decisions for short-term electoral advantage.

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)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.666
Threshold uncertainty score0.566

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.019
GPT teacher head0.261
Teacher spread0.241 · 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
GenreEmpirical

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

Citations0
Published2021
Admission routes3
Has abstractyes

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