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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.005 |
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 it