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Record W2144451987 · doi:10.4309/jgi.2004.10.9

Gambling as activity: Subcultural life-worlds, personal intrigues and persistent involvements <xref ref-type="note" rid="fn1"><sup>1</sup></xref>

2004· article· en· W2144451987 on OpenAlexaffvenue
Robert Prus

Bibliographic record

VenueJournal of Gambling Issues · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicEducation, sociology, and vocational training
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsRealmStatement (logic)EthnographySociologyRelevance (law)PsychologyEpistemologyPsychoanalysisAestheticsPhilosophyPolitical scienceLawAnthropology

Abstract

fetched live from OpenAlex

Although gambling is often envisioned as a disreputable if not also a personally and socially destructive realm of endeavor, this paper approaches gambling as a realm of activity in a more generic, pluralist sense. Employing Henry Lesieur's (1977) portrayal of gambling in The Chase as an ethnographic focal point, this paper not only attempts to "permeate the deviant mystique" that surrounds gambling, but also endeavors to provide a set of conceptual, methodological and textual resources that could inform the study of gambling or other involvements of a parallel sort. Thus, while appreciating the relevance of Henry Lesieur's The Chase for the study of gambling more specifically, this statement also draws attention to the contributions (envisioning Henry Lesieur's text as a prototype) that more sustained and detailed ethnographic studies of gambling as activity can make to the broader social science enterprise. In a related way, whereas more intense gambling often is explained as an individual quality (or affliction), this statement examines gambling more centrally as a subcultural process. Thus, gambling is approached as situated, career, fascinated, and persistent instances of activity that can be adequately understood only within a socially constituted life-world.

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)0.006
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.486
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.002
Open science0.0010.000
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 head0.270
GPT teacher head0.449
Teacher spread0.180 · 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 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

Citations12
Published2004
Admission routes2
Has abstractyes

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