Skilled poker players provide more accurate responses than amateur poker players to the Gambling Fallacies Measure
Bibliographic record
Abstract
Gambling fallacies are mistaken beliefs about how gambling works,<br/>and these form a key part of current theorising about disordered gambling.<br/>However, it has been suggested that key self-report scales for gambling fallacies<br/>may contain items that are inappropriate for skill-based gambling games. This<br/>research explores this topic by comparing amateur and skilled poker players’<br/>responses to the Gambling Fallacies Measure (GFM). Skilled players provided an<br/>average of 8.97 out of 10 accurate responses, which was significantly higher than amateurs’ average score of 6.76. Item five (“A positive attitude or doing good deeds increases your likelihood of winning money when gambling”) was the only item where skilled players (87.9%) were not significantly more accurate than<br/>amateurs (87.1%). Future research along these lines could increase understanding<br/>of the rational cognitions underlying skilled poker play.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".