Twenty Years of Responsible Gambling: The Science-Based Glass is Half Full
Bibliographic record
Abstract
Gamblin disorders • Harm • Minimization • Science based evidenceMore than 20 years ago a group of scholars, gambling executives, and scientists gathered in Reno, Nevada to examine gambling activities in general and the adverse consequences associated with gambling in particular (Blaszczynski et al., 2004).This group established the goal of finding guidelines, processes, and procedures to achieve the objective of determining strategies and activities that could reduce the prevalence of gambling-related harms and prevent its incidence.This work became known as the Reno Model, a blueprint for achieving effective responsible gambling (RG) outcomes.Soon, after the meeting, different gambling-related organizations and operators began to adopt the Reno Model and integrate it into existing and new gambling policies around the world.Despite the clearly stated objectives of RG, many operators claimed the adoption of the Reno Model without subsequently empirically evaluating the impact of activities that stakeholders presumptively considered responsible gambling.One striking example is the widely used logo "Play Responsibly," adopted by many operators around the world who printed or displayed this message on many gambling products, brochures, and venue signs.Fortunately, some international organizations insisted on more empirical evidence to meet different levels of RG certification, such as the World Lottery Association (e.g., https:// www. world-lotte ries. org/ servi ces/ indus try-stand ards/ respo nsible-gaming-frame work/ princ iples).Unfortunately, this context provided opportunities for many operators to adopt a variety of purported RG initiatives without developing sound and rigorous strategies to assess their impact.Furthermore, this situation set the stage for some scientists, and laypeople alike, to make questionable claims (e.g., Handcock & Smith, 2017) about RG and the activities that are associated with RG (e.g., long-term effects of voluntary self-exclusion or the use of various pamphlets to self-assess patrons' gambling activities).Taking into account the short time period since the emergence of RG, and the dramatic expansion of legalized gambling, some scientists strongly questioned the claims of stakeholders regarding the positive effects of RG (e.g., Williams, West, & Simpson, 2012).received funding support from a variety of sources, including the following: Bwin.Party Interactive Entertainment, AG;
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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 | 0.035 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.013 | 0.028 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.011 | 0.024 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".