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Record W2167101444

Cities in Canadian Federalism

2006· article· en· W2167101444 on OpenAlexaboutno aff
Enid Slack

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicLocal Government Finance and Decentralization
Canadian institutionsnot available
Fundersnot available
KeywordsProsperityFederalismReputationGovernment (linguistics)Political economyPolitical scienceRevenueDevelopment economicsEconomicsEconomic growthLawPolitics
DOInot available

Abstract

fetched live from OpenAlex

We consider the place of cities, particularly large cities, in Canadian federalism from several perspectives. Although by most measures the current fiscal condition of Canadian cities seems fairly good, we argue that beneath this happy picture lies a less happy reality. Owing to the limited and relatively inelastic revenue base to which even the largest cities have access, the underlying basis of Canada’s urban prosperity is being eroded, with potentially damaging implications for national well-being over the long run. In an important sense, the roots of this problem lie in the fact that cities do not have any real role or voice in Canada’s federal structure. Since neither role nor voice is likely to be bestowed on them in the near future, however, we conclude by laying out a series of less fundamental actions that all levels of government have to undertake if they wish to maintain not only the present reputation of Canada’s big cities as ‘a nice place to live’ but also, more fundamentally, the urban dynamic that evidence around the world suggests increasingly underpins economic growth.

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 score0.812
Threshold uncertainty score0.942

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.006
Science and technology studies0.0280.008
Scholarly communication0.0080.002
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0170.000

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 head0.009
GPT teacher head0.248
Teacher spread0.239 · 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

Citations9
Published2006
Admission routes1
Has abstractyes

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