A Literature Review and Gap Analysis of Emerging Technologies and New Trends in Gambling
Bibliographic record
Abstract
There have been significant changes in the gambling landscape particularly relating to gambling in the digital age. As the gambling landscape changes, regulation of gambling also needs to change. In 2018, the Office of Responsible Gambling in New South Wales, Australia, commissioned a gap analysis to inform their research objectives and priority focus areas. This included an identification of gaps in our understanding of emerging technologies and new trends in gambling. A gap analysis of the peer-reviewed literature published since 2015 was undertaken, identifying 116 articles. The main area of focus was Internet gambling, followed by articles exploring the relationship between video gaming and gambling, the expansion of the sports betting market, Electronic Gambling Machines characteristics and articles exploring new technologies and trends in advertising and inducements. Key gaps related to the need for more research in general, as well as research focusing on subpopulations such as those using different gambling formats, those with varying levels of problem gambling, and vulnerable populations. From a methods perspective, researchers saw the need for longitudinal studies, more qualitative research and improved outcome measures. The development and testing of a public health approach to addressing the harms associated with gambling in these areas is needed.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".