I am not a gambler, you are a gambler: Distinguishing between tolerable and intolerable gambling
Bibliographic record
Abstract
Abstract Gambling has become a widespread recreational activity in Canada over the past decade. As gambling activities become more accessible and acceptable in our society, it is expected that the rates of gambling will increase, and in turn so too will the rates of problem gambling. It has been suggested that university students are one of the higher risk groups for gambling problems, yet little attention has been paid to this group. The present study was an exploration of students’ experiences of gambling behaviours to understand how gambling is viewed as both tolerable and intolerable deviance. Undergraduate students enrolled at Memorial University of Newfoundland participated (N = 203). Approximately 90% of the students surveyed reported gambling in the last 12 months with 37.1% of the sample reporting some level of risk associated with gambling behaviours. Students were more likely to identify negative motives for the gambling of other people than they were for their own gambling behaviours.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".