How the Internet is Changing Gambling: Findings from an Australian Prevalence Survey
Bibliographic record
Abstract
Interactive gambling as a regulated activity, coupled with easy accessibility to offshore providers represents a new mode and format of gambling superimposed on traditional land-based opportunities. This paper aimed to investigate the prevalence of gambling among Australian adults and the relationship between various gambling activities and interactive modes of access. A second aim was to compare interactive and non-interactive gamblers in terms of socio-demographic characteristics, attitudes and beliefs about gambling and gambling participation. In a nationally representative telephone survey, 15,006 Australian adults completed measures assessing past 12-month gambling participation and a sub-sample completed questions about interactive gambling and beliefs. The majority of participants (64.3 %) reported gambling at least once, with 8.1 % having gambled online. Interactive gamblers gambled on a greater number of activities overall and more frequently. Interactive gamblers were more likely to be male, younger, have home Internet access, participate in more forms of gambling and have higher gambling expenditure. Almost half of the interactive gamblers preferred land-based gambling although a small proportion also noted a number of disadvantages of interactive gambling. This study shows that the nature of gambling participation is shifting with interactive gambling having a significant and growing impact on overall gambling involvement.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".