Predictive Factors of Excessive Online Poker Playing
Bibliographic record
Abstract
Despite the widespread rise of online poker playing, there is a paucity of research examining potential predictors for excessive poker playing. The aim of this study was to build on recent research examining motives for Texas Hold'em play in students by determining whether predictors of other kinds of excessive gambling apply to Texas Hold'em. Impulsivity, negative mood states, dissociation, and boredom proneness have been linked to general problem gambling and may play a role in online poker. Participants of this study were self-selected online poker players (N = 179) who completed an online survey. Results revealed that participants played an average of 20 hours of online poker a week and approximately 9% of the sample was classified as a problem gambler according to the Canadian Problem Gambling Index. Problem gambling, in this sample, was uniquely predicted by time played, dissociation, boredom proneness, impulsivity, and negative affective states, namely depression, anxiety, and stress.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".