Loyalty programmes in the gambling industry: potentials for harm and possibilities for harm-minimization
Bibliographic record
Abstract
The field of gambling studies has been remarkably silent on loyalty programmes in the gambling industry. This article reviews the scant empirical literature, with an aim to stimulate discussion and research about the impact of loyalty programme membership on players. Preliminary evidence suggests that disordered gamblers are more apt to join a loyalty programme and be disproportionately rewarded (due to the amount of money they spend gambling) relative to recreational and at-risk gamblers. As such, loyalty programmes in the gambling industry may generate harms in vulnerable individuals. However, loyalty programmes may also be well positioned to facilitate harm-minimization by promoting behavioural tracking that is collected on every member – information that can be provided to players to advance responsible gambling. Additionally, members could be rewarded for engagement with responsible gambling tools, which may increase the currently low rate of tool use. That said, structuring loyalty programmes to reward the use of responsible gambling instruments with time on device or even non-monetary prizes may be incompatible with harm-minimization efforts. There exists a need for empirical research on the antecedents and consequences of loyalty programme membership as well as the possibility that loyalty programmes have some responsibility gambling utility.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".