Bibliographic record
Abstract
As a popular form of recreational risk taking, gambling games offer a paradigm for decision neuroscience research. As an individual behavior, gambling becomes dysfunctional in a subset of the population, with debilitating consequences. Gambling disorder has been recently reconceptualized as a "behavioral addiction" in the DSM-5, based on emerging parallels with substance use disorders. Why do some individuals undergo this transition from recreational to disordered gambling? The biomedical model of problem gambling is a "brain disorder" account that posits an underlying neurobiological abnormality. This article first delineates the neural circuitry that underpins gambling-related decision making, comprising ventral striatum, ventromedial prefrontal cortex, dopaminergic midbrain, and insula, and presents evidence for pathophysiology in this circuitry in gambling disorder. These biological dispositions become translated into clinical disorder through the effects of gambling games. This influence is better articulated in a public health approach that describes the interplay between the player and the (gambling) product. Certain forms of gambling, including electronic gambling machines, appear to be overrepresented in problem gamblers. These games harness psychological features, including variable ratio schedules, near-misses, "losses disguised as wins," and the illusion of control, which modulate the core decision-making circuitry that is perturbed in gambling 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".