‘Of course we make money, but it has to be in a responsible way’: Safer gambling practices reported by state-owned gambling operators
Bibliographic record
Abstract
Gambling can be regulated in different ways, with some jurisdictions having competitive markets of privately-owned operators, some jurisdictions having state-owned operators that have exclusive legal monopolies, and in other jurisdictions former state-owned monopolies now compete in markets against privately-owned operators. While privately-owned operators tend to emphasize gamblers’ individual responsibility while implementing suboptimal voluntary harm-prevention measures, less is known about the safer gambling practices of state-owned gambling operators, a topic which we aim to contribute to here. Semi-structured interviews were conducted with participants employed in safer gambling roles at state-owned gambling operators in 10 jurisdictions, with transcripts then subjected to thematic and discourse-based analyses. Participants constructed distinctive identities for their organizations. State-owned operators were portrayed as being uniquely capable of balancing profit with consumer protection, of building consumer trust, and in pioneering in harm reduction. The safer gambling practices of privately owned operators were described as ‘performative’, whereas state-owned operators emphasized a more ‘authentic’ approach. This included making safer gambling tools accessible, proactively contacting customers experiencing harm, and implementing operator-driven limits based on risk profiles. When discussing competitive market dynamics, participants challenged dominant narratives about illegal gambling markets. Participants criticized excessive marketing practices by private operators and advocated for system-wide approaches to harm prevention rather than fragmented ones. The perspectives from state-owned gambling operators should be integrated into new harm-prevention approaches for today’s online and interconnected gambling world.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".