Is Gambling an Addiction Like Drug and Alcohol Addiction?: Developing Realistic and Useful Conceptions of Compulsive Gambling
Bibliographic record
Abstract
As compulsive gambling and problem gamblers attract continued and increasing attention - due to state reliance on gambling for revenues and government and private marketing of the gambling experience - conceptions of compulsive, or addictive, gambling have evolved. The disease model of alcoholism and drug addiction, which predominates in the U.S. and North America, has generally been widely adopted for purposes of understanding and addressing gambling problems. However, this model fails to explain the most fundamental aspects of compulsive drinking and drug taking, so it can hardly do better with gambling. For example, people regularly outgrow addictions - often without ever labelling themselves as addicts. Indeed, gambling provides a vivid and comprehensible example of an experiential model of addiction. Elements of an addiction model that gambling helps to elucidate are the cycle of excitement and escape followed by loss and depression, reliance on magical thinking, failure to value or practice functional problem solving and manipulative orientation towards others.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.035 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".