MétaCan
Menu
Back to cohort
Record W2003972503 · doi:10.1504/ijsmm.2008.017190

Urban regimes and sport in North American cities: seeking status through franchises, events and facilities

2008· article· en· W2003972503 on OpenAlexaff
Daniel S. Mason, Gregory H. Duquette

Bibliographic record

VenueInternational Journal of Sport Management and Marketing · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSport managementAdvertisingPolitical scienceBusinessEconomic geographySociologyEconomic growthGeographyPublic relationsEconomics

Abstract

fetched live from OpenAlex

Entrepreneurial cities in North America are strategically using sport for the purposes of marketing communities as attractive places to live, attractive places to visit, and attractive places to invest. However, how sport is used, and who is behind the strategic actions of cities remains a more nebulous concept. This paper focuses on the role of urban regimes ? the networks of political and business elites in cities ? involved in local decision-making strategies related to sport. Three areas of focus, sports franchises, sporting events, and sports facilities, are reviewed in terms of the involvement of both public and private sector interests. In doing so, this article bridges the management of place and the management of sport in an attempt to generate insight into how sport fits into civic strategies, and how cities have sought to gain status through sport-related development initiatives.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.121

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.004
Scholarly communication0.0030.001
Open science0.0000.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.271
Teacher spread0.255 · 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 designQualitative
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

Citations18
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Sport Management and MarketingSame topicSport and Mega-Event ImpactsFrench-language works237,207