The Zone and the Shame: Narratives of Gambling Problems in Japan
Bibliographic record
Abstract
Japan has one of the highest rates of severe gambling problems in the world. However, the gambling forms that cause the most harm—pachinko and pachislot—are not recognized as gambling in the key legislation. They are understood as entertainment. On the basis of two group interviews with those who have experienced problems with gambling, this study explores how they have dealt with the shame, guilt, and stigma of pachinko-related gambling problems. The narrative analysis shows that the participants carry self-stigma as a result of self-reproach and others’ condemnation of their behavior. Feelings of shame, guilt, and fear of being stigmatized have distinctly hindered the process of seeking help. The participants describe how their gambling, which they had attempted to limit, had led to isolation from normal life. The isolation and the failures to control the gambling increased their feelings of shame and destructive behavior. Considering the characteristics of the zone, the loss of self, and the shame, guilt and stigma of failing to control excessive pachinko gambling, it is unreasonable to place the main responsibility on the individual gambler. To reduce gambling harms in Japan and the stigma associated with pachinko and pachislot problems, these gambling forms need to be acknowledged as public health concerns and categorized as gambling in the legislation.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".