A comparative profile of the Internet gambler: Demographic characteristics, game-play patterns, and problem gambling status
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
Overcoming the methodological limitations of many previous studies, the present study employs a two-phased approach to data collection, and a weighted approach to data analysis, thereby obtaining survey data from 1954 Internet gamblers and 5967 non-Internet gamblers. Using this data, the authors examine: (1) the comparative demographic and health characteristics of Internet versus land-based gamblers; (2) the characteristics predictive of Internet gambling; (3) the game-play patterns of Internet gamblers; (4) the comparative gambling expenditures of Internet versus land-based gamblers; and (5) the comparative rate of problem gambling among Internet versus land-based gamblers. The article concludes with a discussion of the methodological implications the present study holds for future research. Moreover, in light of the key finding that Internet gamblers are three to four times more likely to have a gambling problem, the article concludes with a discussion of relevant theoretical and policy implications.
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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 | 0.000 |
| 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) | 0.001 | 0.000 |
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.
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