Impact of gambling advertisements and marketing on children and adolescents: Policy recommendations to minimise harm
Bibliographic record
Abstract
With the proliferation and acceptance of gambling in society, gambling advertisements have become increasingly prominent. Despite attempts to protect minors from harm by prohibiting them from engaging in most forms of gambling, there are few restrictions on the marketing of gambling products. Evidence of high rates of gambling and associated problems amongst youth indicates that the issue of youth gambling must be addressed to minimise harm. This paper aims to examine the current marketing techniques used to promote gambling and how they affect youth. The effect of multiple forms of advertisements will be discussed, including advertising placement in the media, point-of-sale displays, sports sponsorship, promotional products, celebrity endorsements, advertisements using Internet and wireless technology, and content which may appeal to or mislead children. Based on research in gambling and other public health domains, including tobacco, alcohol, and junk food advertising, recommendations are made for appropriate regulations for gambling advertisements to minimise the potential harms.
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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 | 0.019 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.029 | 0.010 |
| Insufficient payload (model declined to judge) | 0.029 | 0.004 |
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".