Differences between Poker Players and Non-Poker-Playing Gamblers
Bibliographic record
Abstract
Since approximately 2003, the popularity of poker has quickly risen to unprecedented heights. This study examined poker play among university students who gamble on a regular basis. A total of 513 undergraduate students (females = 344, males = 170; mean age = 22.1) who gamble in some form at least two times per month completed an online questionnaire; 62.2 per cent (n = 319) of the respondents reported playing poker for money in the past year. A logistic regression analysis showed that poker players were more likely to be male, younger, have higher scores on an index of alcohol abuse, spend more time gambling and gamble more frequently compared to non-poker players. A second logistic regression showed that online/casino poker players were more likely to be male, have higher scores on an index of problem gambling, spend more time and money gambling, and gamble more often compared to social/non-poker players. These results are discussed in terms of the potential of poker's newfound popularity to lead to an increase in addictive behaviours, particularly among adolescents and young males.
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How this classification was reachedexpand
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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".