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Record W4281744349 · doi:10.5130/cjlg.vi26.8141

Regionalism from above: intergovernmental relations in Canadian metropolitan governance

2022· article· en· W4281744349 on OpenAlexafffundabout
Zack Taylor

Bibliographic record

VenueCommonwealth Journal of Local Governance · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Systems and Governance
Canadian institutionsWestern University
FundersUniversity of Toronto
KeywordsMetropolitan areaCorporate governanceRegionalism (politics)Context (archaeology)Political scienceAgency (philosophy)Public administrationRegional scienceEconomic growthSociologyGeographyEconomicsPoliticsDemocracy

Abstract

fetched live from OpenAlex

Robust metropolitan governance is increasingly viewed as necessary to address important economic, social and environmental problems. In this context, this article surveys recent developments in Canadian metropolitan governance. Canada was admired in the post-war period for the effectiveness of its two-tier and unitary metropolitan governments; however, few survive today as urbanisation patterns have become increasingly polycentric and intergovernmental relations more conflictual. Three models have emerged in Canada, sometimes in combination with one another: the multi-purpose regional intergovernmental organisation, the single-purpose metropolitan agency, and the provincial metropolitan policy overlay. Examples of each are discussed, with an emphasis on the interplay of horizontal (intermunicipal) and vertical (provincial–municipal) intergovernmental relations. Ultimately, provincial governments are by virtue of their constitutional authority and spending power the only actors capable of establishing and maintaining durable institutions and policies of metropolitan scope, and they have chosen to do so in Canada’s largest and most urbanised provinces.

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)0.003
metaresearch head score (Gemma)0.006
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 score0.821
Threshold uncertainty score0.952

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0150.011
Scholarly communication0.0080.002
Open science0.0010.003
Research integrity0.0010.002
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 head0.017
GPT teacher head0.292
Teacher spread0.275 · 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

Citations3
Published2022
Admission routes3
Has abstractyes

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