Bibliographic record
Abstract
Canadians, like their neighbors to the south, have been betting on the outcomes of sporting events for many years. Until recently, those wagering on the outcome of a single game have done so either socially, illegally, or in a vast grey market. While many Canadians have had access to parlay-style wagering since the 1980s, single-game wagering has been out of reach until recently. After more than a decade of trying to pass legislation to amend the Criminal Code of Canada, Parliament was finally able to amend the law in 2021, allowing provinces to begin offering wagering on the outcome of individual sporting events. While nearly all provinces turned to their lottery operators, who had previously offered parlay wagering, Ontario announced prior to the amendment’s passage that it intended to open the market to private operators. A little more than six months after first launching single-game sports betting via the province’s lottery corporation, the market opened to private operators. The “grand experiment” remains young, and many questions remain to be answered including whether revenues will match that of a provincially operated monopoly. This article explores the evolution of the legalization of single-game sports wagering in Canada and discusses the emerging market.
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".