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Record W2273431278

Bingo: Winning and Losing in the Discourses of Problem Gambling

2016· article· en· W2273431278 on OpenAlexaboutno aff
Jo‐Anne Fiske

Bibliographic record

VenueProject Muse (Johns Hopkins University) · 2016
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesEthnologyContext (archaeology)SociologyPower (physics)ColonialismPolitical scienceArtGeographyLawArchaeology
DOInot available

Abstract

fetched live from OpenAlex

This study focuses on how popular discourses of problem gambling construct gendered and racialized identities in central British Columbia, a region producing the highest bingo revenues in the province. It explores how bingo discourses emerge as a symbolic resource within a socio-economic context imbued with a colonial legacy of racialized power relations. The goal is to illuminate, through the application of critical discourse methods, how these discourses legitimate local regimes of power through the pathologizing processes that result in stigmatizing children and women as “bingo orphans,” “bingo bags” and “bingo addicts.” Resume: Cette etude se concentre sur les discours populaires des problemes de dependance au jeu en Colombie-Britannique centrale, region qui produit les plus hauts taux de revenus de bingo dans la province, et analyse comment ces discours construisent des identites raciales et de genre. Elle explore comment ces discours s’averent des ressources symboliques qui emergent d’un contexte socio-economique impregne de l’heritage colonial et des relations de pouvoir racialisees. L’objectif est d’eclairer, par des methodes critiques d’analyse, comment ces discours legitiment des regimes locaux de pouvoir qui, par des procedes pathologisants, stigmatisent les enfants et les femmes comme des « orphelins du bingo », des « sacs de bingo » et des « dependants du bingo ».

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.790
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.002
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 head0.065
GPT teacher head0.323
Teacher spread0.258 · 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

Citations5
Published2016
Admission routes1
Has abstractyes

Explore more

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