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Record W2317989836 · doi:10.1123/ssj.2015-0054

Resisting the World-Class City: Community Opposition and the Politics of a Local Arena Development

2015· article· en· W2317989836 on OpenAlexaffabout
Jay Scherer

Bibliographic record

VenueSociology of Sport Journal · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsGrassrootsOpposition (politics)DowntownPoliticsPublic administrationPolitical scienceLeagueDemocracyParliamentSociologyPolitical economyEconomic growthPublic relationsLawEconomics

Abstract

fetched live from OpenAlex

While the public subsidy of major league sport franchises and associated urban development projects remains wildly popular in some constituencies, these expenditures have, increasingly, been met with organized resistance. This article examines the formation of Voices for Democracy (VFD)—a grassroots community group that opposed the use of public funds to build a CAD $606.5 million arena and entertainment district in Edmonton, Alberta. I begin by providing an analysis of VFD’s division of labor and the collective development of the group’s political claims and tactical repertoire to challenge a powerful growth coalition between 2011–2013. Next, I examine the unfavorable political opportunity structure that set decisive limits on what the group could challenge. The article concludes with a discussion of why VFD was unable to cultivate a more widespread coalition of support and, in turn, how the ‘boosterish’ coalition in Edmonton—a coalition that included the Edmonton Oilers, the downtown business community, the mayor and a majority of council, and senior civil servants—were able to contain opposition over the course of this divisive debate.

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)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.217
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.004
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.099
GPT teacher head0.350
Teacher spread0.251 · 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; both teacher heads agree on what is shown here.

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

Citations25
Published2015
Admission routes2
Has abstractyes

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