Grey Literature is a Necessary Facet in a Critical Approach to Gambling Research
Bibliographic record
Abstract
Commercial gambling has seen massive global expansion in the past 25 years. It is a huge industry selling a risky form of entertainment: problem gambling is the only non- substance addiction recognized in the DSM-5, affecting an average of 2.3% of people in jurisdictions where prevalence data are available. Gambling also harms people who gamble below the clinical threshold of "problem gambling", as well as the friends, families and communities of people who gamble. Gambling harm is disproportionally felt by racialized peoples and people of lower socioeconomic status. As such, researchers and governments are increasingly viewing gambling as a public health issue. Gambling research is published in both the primary and grey literature, and the integrity of gambling research is a topic of increasingly heated debate. Bibliometric reviews have found that gambling research is heavily focused on the psychological and biological characteristics of people with problem gambling, with less emphasis on the gambling products themselves and how they are provided. While the gambling grey literature is recognized as valuable by the gambling research community, it has not yet been systematically assessed. In this paper we present the grey literature analysis portion of a pilot project to use a big data approach to produce a mapping review of gambling research from five nations: Australia, Canada, New Zealand, United Kingdom, and United States. For primary research publications on gambling, we performed systematized searches on the Scopus and Web of Science databases. For gambling grey literature, we retrieved all grey literature documents in the GREO International Gambling Research Evidence Centre. For the period of 2014-2018, the grey literature search yielded 360 reports, compared to 1292 articles in the primary literature search. The proportion of grey literature greatly varied by country, ranging from <10% in USA to nearly 50% in New Zealand. Content analysis revealed that the problems investigated in gambling grey literature are very different from the published literature: the top problems in the published literature were young gamblers and online gambling, whereas the top problems in the grey literature were the prevalence of problem gambling and the health and well-being of the public. This demonstrates that the grey literature is a vital piece of the puzzle to understanding this public health issue.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.087 | 0.005 |
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; both teacher heads 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".