The proportion of gaming revenues derived from problem gamblers: Examining the issues in a Canadian context
Bibliographic record
Abstract
The legitimacy of government-sponsored gambling and its continued expansion depends in part on the impact that gambling has on society and the extent to which gambling revenue is derived from vulnerable individuals. The purpose of the present article is to try to establish a valid estimate of the proportion of gaming revenue derived from problem gamblers in Canada. Using recent secondary data collected in eight Canadian provinces, we estimate this proportion to be 23.1%, compared to a problem gambling prevalence rate of 4.2%. This estimate must be seen as tentative, however, as self-reported expenditures are 2.1 times higher than actual provincial gaming revenues. Gambling is an important and expanding economic growth industry in Canada. Net revenue from government-run lotteries, video lottery terminals, and casinos rose from $2.7 billion in 1992 to $11.3 billion in 2002 (Statistics Canada, 2003). Gambling is also a socially acceptable activity, with the large majority of Cana-dians reporting that they gamble at least occasionally (Azmier, 2000; Statistics Canada, 2003). It is not surprising to observe such high proportions of gamblers in light of the many gambling opportunities available to Canadians. Lotteries, instant-win tickets, sports betting (Sports Select), electronic gaming machines ∗Correspondence concerning this article should be addressed to Robert Williams, School of Health Sciences, University of Lethbridge, Lethbridge, Alberta T1K 3M4 [e-mail:
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.017 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".