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Record W7120370449

Integrating transportation and land use planning at the metropolitan level in North America: multilevel governance in Toronto and Chicago

2017· article· pt· W7120370449 on OpenAlexaboutno aff
Fanny R. Tremblay-Racicot, Jean Mercier

Bibliographic record

VenueLA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · 2017
Typearticle
Languagept
FieldSocial Sciences
TopicUrban Planning and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsMetropolitan areaLand useLand-use planningCorporate governanceGovernment (linguistics)PoliticsState (computer science)Regional planning
DOInot available

Abstract

fetched live from OpenAlex

This article compares the policies and processes by which transportation and land use planning are integrated in metropolitan Toronto, Canada, and Chicago, in the United States. Using twenty-four semi-structured interviews with key informants, it describes the array of interventions undertaken by governmental and non-governmental actors in their respective domains to shed light on how the challenge of integrating transportation and land use planning is addressed on both sides of the border. Evidence concerning the political dynamics in Toronto and Chicago demonstrates that the capacity of metropolitan institutions to adopt and implement plans that integrate transportation with land use fundamentally depends on the leadership of the province or the state government. Although the federal government of each nation can bypass the sub-national level and intervene in local affairs by funding transportation projects that include land use components, its capacity to promote a coherent metropolitan vision is inherently limited. In the absence of leadership at the provincial or state level, the resence of a policy entrepreneur or a strong civic capacity at the regional levelcan be a key factor in the adoption and implementation of innovative reforms.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.622
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.045
GPT teacher head0.289
Teacher spread0.244 · 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 designObservational
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
Published2017
Admission routes1
Has abstractyes

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