Predictive Factors of Excessive Online Poker Playing
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
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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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 | 0.001 | 0.001 |
| 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 it