Bibliographic record
Abstract
Abstract Gambling is an instance of risky decision‐making where money is staked on the uncertain prospect of a larger outcome. While commercial gambling is widely recognised to have a negative expected value (generated by the ‘house edge’), gambling is highly prevalent in most countries. It is also a behaviour that becomes dysfunctional for a minority of gamblers, and ‘gambling disorder’ is conceptualised as a form of behavioural addiction. Psychological theories of gambling should explain both the sheer existence of this behaviour within human decision‐making and its potential to become addictive. Contemporary approaches are considered, drawing upon conditioning, cognitive psychology, relevant personality variables and underlying neurobiology. Key Concepts Gambling is a prevalent form of recreational risk‐taking. Gambling disorder has been reclassified recently as an addictive disorder in the DSM‐5 and is thereby the first recognised behavioural addiction. Motivational and cognitive accounts propose that gambling disorder is acquired and maintained through reinforcement learning and distorted beliefs about the probability of events. Impulsivity is a risk factor for gambling disorder, supported by prospective research. Neurobiological studies support the overlap between gambling disorder and substance use disorders, with characteristic changes in dopamine and the brain reward system.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".