Bibliographic record
Abstract
Applying recent research on self-conscious emotions (e.g., Tangney & Dearing, 2002) to the literature of gambling, the proposal that painful self-conscious emotions brought about by chronic awareness of personal inferiority and inadequacy, deemed as a major predisposing factor for problem gambling (Jacobs, 1986), appears to be compatible with the chronic affective trait of shame-proneness but incompatible with guilt-proneness. This premise led to the hypothesis that shame-proneness is strongly associated with problem-gambling severity, whereas guilt-proneness is minimally associated with problem gambling. Further, it was hypothesized that shame-prone gamblers frequently use avoidant coping strategies following gambling loss and chase losses, whereas this tendency is minimal among guilt-prone gamblers. These hypotheses were supported by the data from a retrospective survey of recent gambling loss occasions (N=284). The findings indicate that shame-proneness is one of the predisposing risk factors for problem gambling, whereas guilt-proneness may mitigate gambling problems.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".