“It Always Depends on the Context”
Bibliographic record
Abstract
While it is well recognized that gambling behaviours are shaped by the contexts in which they occur, less research has investigated the relationship between poker and substance use (i.e., alcohol and other drugs). The current study explores poker players’ perceptions of the relationship between substance use and gambling. This qualitative descriptive study is a secondary data analysis of 25 interviews with poker players conducted as part of a broader prospective cohort project. From the thematic analysis, players described how specific contextual factors, such as social setting and location (e.g., bars, casinos) influenced their substance use. Poker players reported a relationship between substance use and gambling practices. However, players differed greatly in their decisions about whether, and how much, to use alcohol and other drugs, with individuals’ choices depending heavily on contexts (e.g., more inclined to partake when alcohol was available) and motivations (e.g., remaining sober to remain sharp and not impair their intellectual capacity). For those players who considered poker earnings to be their main source of income, increased use of alcohol, tobacco and cannabis were reported as a way of dealing with stress, anxiety and a lack of motivation related to their play.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".