Bibliographic record
Abstract
By applying Foucault’s genealogical approach, this article understands the ascension of the medical model of problem gambling as a happenstance and contingent effect of a new form of social control (biopower). The investigation reveals the cumulative effect of some of the heterogeneous components surrounding the medical model’s creation: discourses; institutions; laws; regulatory decisions; administrative measures; scientific proposition, and philanthropic, moral, and philosophical arguments. In the process, it becomes apparent that the medical model is an effect of a form of control that is embedded in the population itself as a norm and follows the schemata of confessional discourse. This power is disciplining individual bodies and regulating populations towards normality by making problem gamblers critically examine themselves and discursively reveal the results. However, the present subjectivity for problem gamblers (i.e., how they understand themselves and how they are understood by those who would improve them) is an effect of the type of power contained in the confession as well. A certain form of subjectivity is created by admitting ‘I am powerless over gambling.’ While the language problem gamblers use to describe themselves is a mere effect of power, it nevertheless determines how they think of themselves and their relationship with gambling.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.026 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".