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Record W2065989902 · doi:10.1002/pad.386

The governance of metropolitan areas in Canada

2005· article· en· W2065989902 on OpenAlexaffabout
Andrew Sancton

Bibliographic record

VenuePublic Administration and Development · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Systems and Governance
Canadian institutionsWestern University
Fundersnot available
KeywordsMetropolitan areaCorporate governanceGovernment (linguistics)Regional sciencePublic administrationLocal governmentPolitical scienceBusinessGeographyFinance

Abstract

fetched live from OpenAlex

Abstract This article briefly examines five significant Canadian developments with respect to the governance of metropolitan areas: annexations and mergers such that there is one main municipal government for the metropolitan area, two‐tier metropolitan government, the amalgamation of two‐tier metropolitan systems into a single municipality, demergers in Quebec, and the creation of flexible and innovative entities for metropolitan governance. Special attention is paid to the Greater Toronto Area, a continuous built‐up urban area that transcends at least three metropolitan areas as defined by Statistics Canada. In the absence of any authority covering the entire metropolitan area, it now appears that the Ontario provincial government is becoming the key policy maker. As an example of a flexible and innovative form of metropolitan governance, the Greater Vancouver Regional District merits attention elsewhere in the world. Canada's experiences with so many different institutional arrangements in recent years means that there is much to be learnt from their obvious failures and occasional successes. Copyright © 2005 John Wiley & Sons, Ltd.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.100
Threshold uncertainty score0.726

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0070.004
Scholarly communication0.0050.001
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.021
GPT teacher head0.273
Teacher spread0.252 · 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 source (direct Gemma or distilled Codex), 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

Citations59
Published2005
Admission routes2
Has abstractyes

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