The attractions and risks of Internet gambling for women: A qualitative study
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
In this qualitative study, 25 females were interviewed who gambled frequently on the Internet. This paper describes the women's views about the Internet as a place to gamble and the associated risks. Volunteers were recruited from a wide range of sources in the UK and included 16 problem gamblers and 9 frequent gamblers. The women identified a number of features of the Internet that made it easy to gamble, such as its accessibility from home, its anonymity, and its privacy. The Internet was seen as less of a male domain and a place where women could learn to gamble. Frequent gamblers saw Internet gambling as a fun and social activity. All women recognised that they were at risk of excessive Internet gambling, and frequent gamblers had developed strategies to reduce these risks. The paper concludes with some measures that could identify and support those at risk.
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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 | 0.002 | 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) | 0.000 | 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