Bibliographic record
Abstract
The reclassification of gambling disorder within the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) addictions category marks an important step for addiction science. The similarities between gambling disorder and the substance use disorders have been well documented. As gambling is unlikely to exert actively damaging effects on the brain, the cognitive sequelae of gambling disorder may provide insights into addictive vulnerabilities; this idea is critically evaluated in light of recent structural imaging data. The second part of the review analyzes a fundamental question of how a behavior can become addictive in the absence of exogenous drug stimulation. The relative potency of drug and nondrug rewards is considered, alongside evidence that cognitive distortions in the processing of chance (for example, the illusion of control and the gambler's fallacy) may constitute an important added ingredient in gambling. Further understanding of these mechanisms at neural and behavioral levels will be critical for the classification of future behavioral addictions, and I consider the current research data for obesity and binge eating, compulsive shopping, and internet gaming disorder.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".