Why do gamblers over-report wins? An examination of social factors
Bibliographic record
Abstract
The role of social factors in gamblers' over-reporting of wins was explored using a survey administered via the Internet. One hundred and fifteen gamblers (average age 36.9) completed the survey. The majority of gamblers reported that they do not over-report wins, and would not do so for social reasons. However, they believe that other gamblers do mislead people about their losses for a variety of social reasons, such as a desire to appear skilled or to be popular. As well, the majority of gamblers report not feeling urges to gamble when hearing about wins, although younger people, males, and those with gambling problems were significantly more likely to report feeling and/or acting on urges to gamble when hearing about others' wins. The discrepancy between their views of themselves and of other gamblers may be due to cognitive distortions specific to gamblers, or may reflect a general self-presentation bias.
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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 | 0.001 | 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 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".