‘It's a tradition to go down to the pokies on your 18th birthday’ – the normalisation of gambling for young women in Australia
Bibliographic record
Abstract
OBJECTIVE: To understand the range of factors that may influence the normalisation of gambling for young women in Victoria, Australia. METHODS: In-depth qualitative telephone interviews with 45 women aged 18-34 years. RESULTS: Young women were exposed to gambling environments and some were gambling from an early age. Family members were the key facilitators of these activities. Once reaching the legal age of gambling, peers and boyfriends were instrumental in young women's gambling practices. Women attributed the normalisation of gambling to excessive marketing, feminised gambling environments, and the widespread availability of gambling in the community. CONCLUSIONS: This study found several factors that influenced and encouraged young women to gamble, such as the feminisation of gambling products and environments, and determined that gambling is becoming a socio-culturally accepted activity for young women. Implications for public health: Researchers and policymakers should be increasingly focused on how different forms of gambling may be normalised for young women. Attention should be given to how young women may become a target market for the gambling industry, and how to implement strategies aimed at preventing any future potential harm posed by these industries and their marketing tactics and products.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".