Funding of Gambling Research: Ethical Issues, Potential Benefit and Guidelines
Bibliographic record
Abstract
There has been an unprecedented growth of legalized gambling opportunities in Canada over the past two decades, partly to generate revenues without raising taxes. Unfortunately, for 2-3% of the Canadian population, gambling can become disordered (i.e. develop into a gambling addiction). To help attenuate the harms and prevalence of disordered gambling, all provincial governments earmark a portion of gambling revenues for the prevention, treatment and research into disordered gambling. However, the field of gambling studies has recently come under criticism in the way research is conducted. At the forefront of the criticism is the issue of accepting funding from the gambling industry. We provide an overview of the ethical considerations, potential ethical issues, and the possible benefits of accepting such funding. The aim of the present paper is not to argue for or against accepting industry funding, but rather to delineate the potential ethical issues and benefits related to that acceptance. More importantly, we provide a summary of best practice ethical guidelines, and recommendations to guide in the ethical decision making process in accepting or declining funding from gambling industry. To this end, we use the Canadian Code of Ethics for Psychologists as a framework in which to situate our guidelines and recommendations. Given that Canadian researchers have a long history and continue to contribute valuable knowledge in the field of gambling studies, it is of important for gambling researchers to be aware of the ethical considerations and issues related to funding from gambling industry.
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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 | 0.333 | 0.534 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.013 | 0.036 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.010 | 0.008 |
| Research integrity | 0.037 | 0.032 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".