Psychological Reports, 2004,95,747-753. O Psychological Reports 2004 LEGALIZED GAMBLING AND CRIME IN CANADA1
Bibliographic record
Abstract
Summay.-In the 10 provinces and 2 territories of Canada in 2000, but not in 1990, the total number of types of gambling activities was positively associated with rates of robbery ( p <.05). Controls for other social variables did not eliminate these associations. With so many correlations in the present study the likelihood of a Type I error was quite large. Alpha was adjusted to control that likelihood. Statistical analysis now required even stronger evidence before concluding that there were significant re-lationships between crime and gambling variables or among gambling variables. In the 10 provinces of Canada in 1999/2000, the total numbers of electronic gambling ma-chines for each province was associated with rates of theft over $5000 (p<.01). In 1990 there were positive associations found for burglary with off-track betting and race/sportsbooks; motor vehicle theft with off-track betting, and race/sportsbooks; rate of theft with casinos; quarter horse racing with thoroughbred racing. In 2000 there were positive associations for robbery with casinos and slot machines; casinos with slot machines; scratch tickets with raffles, break-open tickets, sports tickets, and charitable bingo; raffles with break-open tickets, sports tickets, and charitable bingo;
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.078 | 0.007 |
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 source (direct Gemma or distilled Codex), 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".