Episodic Cessation of Gambling: A Numerically Aided Phenomenological Assessment of Why Gamblers Stop Playing in a Given Session
Bibliographic record
Abstract
The topic of permanent cessation of gambling behavior has received increased attention as the rates gaming (and pathology) increase with accessibility and legalization. Despite this increased attention there is a paucity of research on why people stop gambling in a given session, i.e. episodic cessation. We propose that the study of first-person experiential accounts of why gamblers stopped engaging in play within a given session will shed light on the progression and maintenance of wagering behavior. Using numerically aided phenomenology, we systematically examined accounts of episodic cessation. In doing so, we were able to identifying recurrent themes and then clustering these accounts according to similarities in theme profiles. People reported that episodic cessation occurred because they had lost all their money or because they were forced to (Cluster I), a sufficient amount of money had been won or lost (Cluster II), and a priori limits on wins or losses had been reached (Cluster III). As predicted, gamblers with maladaptive reasons for episodic cessation (Cluster I and II) reported more illusory perceptions of control and negative attitudes toward treatment seeking than those who engage in responsible gambling behavior (Cluster III). Moreover, illusions of control mediated the effect of cluster membership on attitudes toward treatment seeking. The findings of the present research help to integrate recent studies of gambling progression and maintenance.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".