The Relationship Between Unexpected Outcomes and Lottery Gambling Rates in a Large Canadian Metropolitan Area
Bibliographic record
Abstract
The purchase of lottery tickets is widespread in Canada, yet little research has directly examined when and why individuals engage in lottery gambling. By leveraging a large urban dataset of lottery sales in Toronto, Canada, and using a simple computational framework popular in psychology, we examined whether city residents gamble more when local outcomes are better than expected; for example, wins by local sports teams or amounts of sunshine based on recent weather history. We found that unexpectedly sunny days predict increased rates of fixed-prize lottery gambling. The number of local sports team wins also predicted increased purchase rates of fixed-prize lottery, but unexpected positive outcomes in sports did not. Our results extend previous findings examining the linkage between sunshine and gambling in metropolitan areas beyond the US, but do not fully replicate the previously observed relationships between unexpected sports outcomes and gambling in US cities. These results suggest that the observed malleability of lottery gambling in response to incidental events in the gambler’s environment may vary considerably across geographies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 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) | 0.003 | 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".