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Municipal shared service collaboration in the Alberta Capital Region: The case of recreation

2008· article· en· W1826204972 on OpenAlexaffabout
Edward C. LeSage, Melville McMillan, Neil Hepburn

Bibliographic record

VenueCanadian Public Administration · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Policy and Administration Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRecreationPolitical sciencePopulationGeographyHumanitiesSociologyDemography

Abstract

fetched live from OpenAlex

Abstract: This article examines the determinants of participation of Edmonton‐area municipalities in shared services arrangements for recreational and cultural services. Potential determinants emerge from the literature on inter‐municipal collaborative arrangements, but this analysis extends the empirical work on the determinants of participation to encompass small municipalities and to consider the appeal of potential partners. The major finding is that, in the Edmonton environment (and likely in many others), municipal population size is the critical determinant of participation, and participation is inversely related to population size. Sommaire : Le présent article examine les facteurs déterminants de la participation des municipalités de la région d'Edmonton dans les ententes relatives aux services partagés en ce qui concerne les services récréatifs et culturels. Les déterminants potentiels proviennent de la littérature sur les ententes de collaboration entre les municipalités. Cependant, l'analyse prolonge le travail empirique sur les facteurs déterminants de la participation pour y inclure les petites municipalités et pour tenir compte de l'appel à des partenaires potentiels. Le principal résultat est que, dans la région d'Edmonton (et vraisemblablement dans de nombreuses autres régions), la taille de la population municipale est le déterminant critique de la participation, et la participation est inversement proportionnelle à la taille de la population.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.920
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.090
GPT teacher head0.367
Teacher spread0.277 · 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.

Study designTheoretical or conceptual
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
Published2008
Admission routes2
Has abstractyes

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