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Record W2046445711 · doi:10.1080/01639625.2012.726178

Gambling in Detention: A Source of Violence?

2013· article· en· W2046445711 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueDeviant Behavior · 2013
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsPrisonSolidarityCriminologyPsychologyPerspective (graphical)Subculture (biology)Social psychologySociologyPolitical sciencePoliticsLaw

Abstract

fetched live from OpenAlex

Although the prison subculture encourages solidarity and mutual support, this does not suffice to control all behaviors inside the walls, since physical and verbal aggressions still occur between inmates. Gambling in prison leads to certain frictions, such as quarrels or threats. However, these frictions are better explained by the characteristics of the prison environment. The real problem would not lie necessarily in the inmates' gambling habits, but rather in the tensions that exist inside the walls and that influence their behaviors. That being said, conflicts similar to those associated with gambling have been observed in other, non-betting, leisure activities. This is notably what emerged from the analysis of 51 interviews conducted with male inmates in three federal penitentiaries in Quebec. This article takes a dynamic look at the physical and verbal aggressions surrounding gambling and puts them into perspective with the reality of the prison environment.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.195
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.096
GPT teacher head0.376
Teacher spread0.280 · 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