Bibliographic record
Abstract
Gambling problems have been linked to a number of characteristic cognitions. People often use justifications to make desirable choices, but little is known about these justificatory thoughts in gambling. We conducted an exploratory Study 1 (n = 101) and a confirmatory Study 2 (n = 154) using online surveys, recruiting gamblers with prior and current experience of trying to reduce their gambling. Using justifications recognized in the domains of eating and consumer behavior (e.g., prior use of effort, feelings of achievement, perceptions of disposable funds), we examined whether justifications were associated with problem gambling severity, and whether they explained additional variance above trait impulsivity and cognitive distortions. In both studies, justifications were positively associated with problem gambling severity, after accounting for trait impulsivity and cognitive distortions. Additionally, justifications were positively correlated with trait urgency and cognitive distortions, indicating that such thinking may not be antithetical to impulsivity. These data provide proof-of-principle evidence that justificatory thinking occurs in the context of gambling and is related to problem gambling severity and may therefore represent a neglected aspect of gambling-related cognitions.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".