Gambling, gambling activities, and problem gambling.
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
This research examined similarities and differences between gambling activities, with a particular focus on differences in gambling frequency and rates of problem gambling. The data were from population-based surveys conducted in Canada between 2001 and 2005. Adult respondents completed various versions of the Canadian Problem Gambling Index (CPGI), including the Problem Gambling Severity Index (PGSI). A factor analysis of the frequency with which different gambling activities were played documented the existence of two clear underlying factors. One factor was comprised of Internet gambling and betting on sports and horse races, and the other factor was comprised of lotteries, raffles, slots/Video Lottery Terminals (VLTs), and bingo. Factor one respondents were largely men; factor two respondents were more likely to be women and scored significantly lower on a measure of problem gambling. Additional analyses indicated that (1) frequency of play was significantly and positively related to problem gambling scores for all activities except raffles, (2) the relationship between problem gambling scores and frequency of play was particularly pronounced for slots/VLTs, (3) problem gambling scores were associated with playing a larger number of games, and (4) Internet and sports gambling had the highest conversion rates (proportion who have tried an activity who frequently play that activity).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it