Mapping Gambling Research in Three Regulatory Environments, 2008-2017
Bibliographic record
Abstract
This study was undertaken by Greo Evidence Insights (formerly Gambling Research Exchange Ontario) to create a mapping review of gambling studies. A mapping review is a type of review article that aims to describe and categorize knowledge within a topic of known scope in order to identify research gaps. There have been very few mapping reviews concerning the whole field of gambling studies, and this is the first to specifically examine the concept of harm. For this study, the authors used the Conceptual Framework of Harmful Gambling (CFHG) as the framework for categorizing gambling research articles. The CFHG describes eight factors contributing to harmful gambling, each with multiple subfactors. The authors searched the Web of Science (WoS) database for gambling research articles from Australia, Canada, and New Zealand, published between 2008 and 2017. These three countries were chosen because they represent three different forms of gambling regulation, described in detail in the article. By following the search strategy, 1,424 articles were retrieved that could be ascribed to a CFHG factor. Each article was assigned a CFHG factor, and if possible, a CFHG subfactor, a secondary factor, and a secondary subfactor. Also recorded are the country and state/province/territory/region or each author from the three target countries, and whether or not the word "harm" is present in the title, abstract, or keywords. The dataset also contains the author, year, title, journal, and various other bibliographic fields that were downloaded from WoS.
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 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 | 0.011 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.052 | 0.058 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".