Gambling Advertising and Incidental Marketing Exposure in Soccer Matchday Programmes
Bibliographic record
Abstract
Gambling is marketed in English soccer across various formats such as TV advertising, social media, pitch side hoardings, and shirt sponsorship. There have been recent reductions in TV advertising brought about by self-regulation, but gambling shirt sponsorship remains frequent, and can lead to a high frequency of incidental marketing exposure on TV. Knowledge is lacking on how gambling advertising frequency and marketing exposure have changed over time in other media, such as in matchday programmes. This study addressed this gap via a content analysis of programmes for 44 teams across 3 periods spanning 18 months (N=132). The number of gambling adverts decreased from 2.3 to 1.3 per-programme, while incidental exposure prevalence stayed constant, at a higher rate of 42.7 incidences per-programme. Teams sponsored by gambling companies had more adverts per-programme than those sponsored by other industries (2.3 versus 1.2), and also had more incidental exposure (58.8 versus 20.2). Incidental exposure to gambling marketing was consistently more prevalent (42.7) per-programme than alcohol (3.2) or safer gambling messages (3.1). Furthermore, across all timepoints, 56.8% of dedicated children’s sections contained incidences of gambling marketing. Researchers and policymakers should consider that sports fans can get exposed to gambling marketing through a number of channels outside of TV advertising. Indirect and incidental exposure to gambling marketing remains high, which can be particularly challenging for those experiencing gambling related harm. All forms of gambling marketing must be considered when making legislative changes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".