Gambling advertisements in Ontario: exploring the prevalence and regulatory compliance of televised and social media marketing during sports matches
Bibliographic record
Abstract
This paper reports the results of two studies into the prevalence of gambling advertisements exposed during five NHL and two NBA matches broadcast in Ontario, and the social media advertisements posted by ten Ontario-licensed gambling operators on X/Twitter between the 25th and the 29th of October 2023. The studies found a total of 4,119 gambling messages, consisting of 3,537 television-based gambling references and 582 X/Twitter advertisements. Just over half of the television-based references (50.4%) appeared on the playing surface, while references were visible for 21.7% of the entire duration of broadcasts. Despite this high volume of advertising, none of the references breached Ontario-based standards. The X/Twitter advertisements generated 5,687,087 views while featuring mostly males (78.5%) and individuals aged between 25 and 34 (50.5%). Just under half (48.5%) were identified as content marketing, which were found to be breaching advertising standards by concealing the fact that they were advertisements. The studies were observational, and caution should be taken when interpreting the findings against possible impact on behavior. Nonetheless, the findings highlight the need for the further development of standards to ensure the protection of audiences against a high volume of marketing that risks the possible normalization of gambling within sport.
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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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".