A frequency and content analysis of gambling advertising shown during the FIFA Women’s World Cup 2023
Bibliographic record
Abstract
The growing popularity of women's football could provide an opportunity for gambling operators to target a new and potentially untapped audience, however, there has been comparatively less research on gambling advertising in women's sports compared to men's.This study therefore sought to examine the frequency and content of TV gambling adverts during UK coverage of the FIFA Women's World Cup (WWC) in 2023.All matches were recorded on Sky TV, and variables from each match were coded, including the company responsible, the products advertised, the type of each gambling advert, and the use of safer gambling messages.During 32 matches, 19 gambling-affiliated adverts were coded (M = 0.6).Only brand awareness, and financial inducements advert types featured.Just three of these adverts (15.8%) embedded a safer gambling message, "Take time to think".Adverts featured luck-based games, e.g., lottery or slots; games that have been reported to be popular amongst women, but there were no adverts from female focused gambling operators, suggesting a limited use of gendered advertising.Inconsistencies in advert types between the two coders uncovered regional differences in the content of observed gambling adverts.This raises methodological challenges which would need to be addressed to conduct future research on TV gambling advertising.This study is of significance as it is the first to examine gambling advertising in women's football.Future research should continue to monitor women's sport as a potential marketing opportunity for the gambling industry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".