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Record W2763867983 · doi:10.1177/1078087416684380

The Local Autonomy of Canada’s Largest Cities

2016· article· en· W2763867983 on OpenAlexafffundabout
Alison K. Smith, Zachary Spicer

Bibliographic record

VenueUrban Affairs Review · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Systems and Governance
Canadian institutionsBrock UniversityUniversité de Montréal
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsAutonomyLegislatureLocal governmentDecentralizationPoliticsPublic administrationPolitical scienceGovernment (linguistics)Political economySociologyLaw

Abstract

fetched live from OpenAlex

Canada’s cities operate within restrictive legislative frameworks, yet incremental changes have resulted in some large Canadian cities accepting more policy responsibility. In this article, we ask if some big cities have a greater degree of local autonomy than others? We use existing Canadian and international literature to build a made-for-Canada index to quantitatively measure and compare levels of local autonomy by measuring vertical relations between 10 large cities and their respective provinces across three dimensions: legal-administrative autonomy, fiscal autonomy, and political autonomy. Overall, we find low levels of local autonomy, but differences along various dimensions of autonomy, notably political. Few countries in the world have senior levels of government that have been so resistant to loosen restraint and regulation as has been in the case in Canada; our results from this unique and important case shed new light on the reactions of subnational government to evolving demands for increased decentralization and local autonomy.

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)0.002
metaresearch head score (Gemma)0.005
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: Empirical
Teacher disagreement score0.930
Threshold uncertainty score0.508

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0080.007
Scholarly communication0.0060.001
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.013
GPT teacher head0.247
Teacher spread0.234 · 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

Citations16
Published2016
Admission routes3
Has abstractyes

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