Gambling-gaming convergence: new developments and future directions
Bibliographic record
Abstract
The distinction between gambling and gaming activities has become increasingly blurred. One of the principal causes of this is technological convergence, a term which refers to the growing co-location and inter-relationship between different classes of activities. Some argue that convergence may increase the appeal and accessibility of gambling to susceptible or vulnerable individuals. Studies into the nature and effects of convergence have expanded considerably over the last decade. However, researchers and policymakers have often struggled to keep abreast of the pace of technological development and of the breadth of topics that now emerge within this research area. To address these issues, we present this special issue, which highlights new developments in gambling-gaming convergence research. Important topics include: social casino games, simulated Internet gambling, skill-based gaming machines, gambling mechanisms on Twitch.tv, substance use across gambling and gaming activities, and, the extent to which gaming might act as a potential ‘gateway’ to gambling. A common theme was that new technologies are constantly enabling innovations and changes to gambling opportunities, which can affect some vulnerable users involved in these activities. We outline further research avenues to better understand the impacts of digital gambling technologies and to support appropriate regulatory and public health responses.
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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 | 0.016 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.019 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".