The Relationship between Legal Gambling and Crime in Alberta
Bibliographic record
Abstract
One of the main justifications used for the expansion of legal gambling is that gambling provides increased revenue to governments and community groups. However, critics argue that the social costs of legal gambling offset these benefits. One particularly controversial social cost of gambling is the impact that gambling has on crime. The academic literature is split with as many studies showing an increase in crime due to gambling as those that show no impact. The current study investigated how increased legal gambling availability has affected crime in Alberta. Four sources of data were examined: self-reports of gambling-related crime among problem gamblers in population surveys; gambling-related crime in police incident reports; uniform crime statistics from Statistics Canada; and criminal offences as recorded by the Alberta Gaming and Liquor Commission (AGLC). The most unambiguous findings of this study are that gambling-related crime constitutes a very small percentage of all crime; crime that is gambling related tends to be non-violent property crime; and increased legal gambling availability has significantly decreased rates of illegal gambling. In terms of the impact of legalized gambling on overall crime in Alberta, the evidence would suggest that legalized gambling likely has a minor or negligible impact.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".