Bibliographic record
Abstract
Ask any gambler how much money they spend on gambling in a typical year and you’ll almost certainly see a quizzical look appear on their face. Individuals are frequently reluctant to disclose such information and those that do typically find it difficult to recall the specifics of their gambling spending. Gamblers who are willing and able to answer might also need some clarification since the question could be referring to either the cumulative amount of dollars gambled or the net dollar figure gambled after accounting for wins and losses[1]. But what if, instead of asking individual gamblers about their spending, one was attempting to determine gambling spending for the entire country of Canada including provinces and territories… are these figures even available? Are provincial and territorial gambling regulators and operators forthcoming with this information? The short answer is that, yes, it is indeed possible to determine a figure for Canada’s net commercial gambling revenue using available data[2]. In this article, I’ll describe my rationale for documenting available Canadian gambling statistics, methods employed, and challenges encountered. A selection of charts is interspersed throughout to illustrate key gambling statistics using examples from the Canadian Gambling Statistics (1970-2020) online database which was created to house these collected statistics and make them publicly accessible. [1] To learn about these intricacies, see Wood & Williams (2007) ‘How Much Money Do You Spend on Gambling?’ The Comparative Validity of Question Wordings Used to Assess Gambling Expenditure and Auer & Griffiths (2017) Self-Reported Losses Versus Actual Losses in Online Gambling: An Empirical Study. [2] Calculate at $14.51-billion in 2019 or about $500 per Canadian adult (18+ years of age) – for details, see Figure 1.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.021 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.008 |
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".