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Record W3183436711 · doi:10.36939/cjur/vol30no1/art326

Public policy tools to implement transit-oriented development: The case of the Montreal city-region

2021· article· en· W3183436711 on OpenAlexafffundvenueabout
Juliette Maulat, Florence Paulhiac Scherrer, Franck Scherrer

Bibliographic record

VenueCanadian journal of urban research · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicFrench Urban and Social Studies
Canadian institutionsUniversité de MontréalUniversité du Québec à Montréal
FundersSocial Sciences and Humanities Research Council of CanadaUniversité de Montréal
KeywordsMetropolitan areaIncentiveUrban planningTypologyCorporate governanceGovernment (linguistics)Public administrationBenchmarkingPolitical scienceBusinessPublic policyRegional planningRegional sciencePublic relationsEconomic growthEngineeringSociologyEconomicsFinanceMarketingGeography

Abstract

fetched live from OpenAlex

Over the last 25 years, Transit-oriented development (TOD) has become one of the core models of planning in NorthAmerican cities. However, the implementation of TOD is still hindered by various barriers (institutional, political, economical, etc.) leading cities to develop specific policy tools to support TOD. But while TOD has generated a substantialbody of research, literature has paid little attention to its implementation processes, from metropolitan-scale planning to local urban development projects, and the policy tools designed by governments to support TOD. To fill this gap, this paper shifts the focus by analysing the public policy tools used to support TOD implementation, based on a case study of Montreal. It argues that the inclusion of TOD in metropolitan-scale planning has led metropolitan government to develop new public action tools to support the implementation of TOD. It proposes a typology of these tools (regulatory, informative, institutional, good practices and project-based) and examines their differentiated uses and implementation. It demonstrates that these instruments—mostly incentives—are both mirrors and vectors of changes in the planning and governance of the Montreal city-region, favoring collective learning about TOD, new inter-institutional cooperation and shifts in planning practices. This work thus provides empirical evidence of the renewal of planning strategies and the diversification of public policy tools associated with transi

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.656
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.259
GPT teacher head0.374
Teacher spread0.115 · 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.

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

Citations3
Published2021
Admission routes4
Has abstractyes

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