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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".