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Record W2507561447 · doi:10.1123/ssj.2015-0145

The Uses of an Inner-City Sport-for-Development Program: Dispatches From the (Real) Creative Class

2016· article· en· W2507561447 on OpenAlexaffabout
Jay Scherer, Jordan Koch, Nicholas L. Holt

Bibliographic record

VenueSociology of Sport Journal · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDeindustrializationNeoliberalism (international relations)NegotiationPovertyRecreationSolidarityEconomic growthSociologyPolitical scienceUrban planningCapitalismPoliticsGender studiesEconomyPolitical economySocial scienceEngineeringEconomics

Abstract

fetched live from OpenAlex

As a result of a rapidly changing global political economy, deindustrialization, and neoliberalism, a new form of racialized urban poverty has become concentrated in the inner cities of innumerable North American urban centers. In response to these material conditions, various nonprofit organizations, corporate-sponsored initiatives,and underfunded municipal recreation departments continue to provide a range of sport-for-development programs for the ‘urban outcasts’ of the global economy. While sport scholars have widely critiqued these initiatives, little is known about how people experience these programs against the backdrop of actually existing neoliberalism (Brenner & Theodore, 2002) and the new conditions of urban poverty. As part of a three-year urban ethnography in Edmonton, Alberta, this paper examines how a group of less affluent and often homeless young men experienced and made use of a weekly, publicly funded floor-hockey program. In so doing, we explore how this sport-for-development program existed as a ‘hub’ within a network of social solidarity and as a crucial site for marginalized individuals to negotiate, and, at times, resist conditions of precarious labor in a divided Western Canadian city.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.184
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.065
GPT teacher head0.366
Teacher spread0.301 · 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 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

Citations22
Published2016
Admission routes2
Has abstractyes

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