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Record W7008517773

Challenge of defining a national urban strategy in the context of divergent demographic trends in small and large Canadian cities.

2008· article· en· W7008517773 on OpenAlexaboutno aff

Bibliographic record

VenueWestern CEDAR (Western Washington University) · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Public policySample (material)Quality (philosophy)Empirical researchInclusion (mineral)Social securityEmpirical evidence
DOInot available

Abstract

fetched live from OpenAlex

Once forgotten as an object of research, a growing literature dealing with various aspects of small cities has emerged since the new millennium. The answer to the question "does size matter?" has so far received positive empirical support on both sides of the Atlantic. Using the Federation of Canadian Municipalities (FCM) three quality of life studies as backdrop, this paper offers further evidence that small Canadian cities are worth our attention. Since 1999, FCM has extrapolated results from its series of quality of life studies carried out on a sample of large and medium sized cities to monitor key changes in the quality of life of Canadian urban residents. Conclusions drawn from these studies have been used to define a common Canadian municipal agenda which identifies air pollution, public transportation, affordable housing, homelessness, social inclusion and integration, and community safety and security as some of Canada's key urban policy priorities. Following the evolution of a number of key demographic indicators in larger and smaller Canadian cities between 1996 and 2006, this research questions whether the municipal agenda derived from FCM's quality of life studies offers a fair and just reflection of the reality and of the public policy priorities of smaller urban municipalities.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.670
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.033
GPT teacher head0.205
Teacher spread0.172 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2008
Admission routes1
Has abstractyes

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