Setting Limits: Gambling, Science and Public Policy—summary of results
Bibliographic record
Abstract
The gambling industry has grown into a global business in the 21st century. This has created the need for a new emphasis on problem prevention. This article highlights the core themes of the book Setting Limits: Gambling, Science and Public Policy, taking a broad view of the consequences of gambling for society as a burden on health, well-being and equality. The book covers the extent of gambling and gambling-related problems in different societies and presents a critical review of research on industry practices, policy objectives and preventive approaches, including services to people suffering from gambling and its consequences. It discusses the developments in game characteristics and gambling environments and provides evidence on how regulation can affect those. Effective measures to minimize gambling harm exist and many are well supported by scientific evidence. They include restrictions on general availability as well as selective measures to prevent gamblers from overspending. The revenue generated from gambling for the industry, governments, and providers of public services funded from gambling returns presents an obstacle to developing policies to implement harm-reduction measures. A public interest approach must weigh these interests against the suffering and losses of the victims of gambling.
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 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".