Bibliographic record
Abstract
People with gambling problems gamble despite resolutions to stop. These choices may be more likely in contexts that allow for them to be justified (e.g., after a productive day at work), termed the licensing effect. This has not been tested in the domain of gambling. Using a daily diary design across 21 days (n participants = 156, n reports = 2,516), gamblers trying to reduce their gambling reported their daily justification opportunities (e.g., feelings of effort and achievement) and whether they gambled, as well as daily aspects of self-control (i.e., craving, conflict, suppression) and affect (positive and negative). Gambling occurred on 33% of the reported days. Prior-day justification opportunities were associated with a higher likelihood of gambling. Prior-day suppression showed a similar effect, whereas prior-day negative affect showed the opposite effect. After gambling, people experienced stronger cravings, weaker suppression, and poorer well-being (lower positive affect and higher negative affect). Our findings show that people may use justifications to gamble, despite its negative consequences, indicating that the licensing effect may, in part, explain why people gamble despite resolutions to stop.
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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 | 0.009 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".