“Help Is the Sunny Side of Control”: The Medical Model of Gambling and Social Context Evidence in Canadian Personal Bankruptcy Law
Bibliographic record
Abstract
At the start of the twentieth century, people who gambled excessively were viewed as morally deviant. Now, they are viewed as suffering from a medical disorder. Legal actors incorporate this medical approach to gamblers into how they apply the law. This shift from a moral to a medical model reorients actors from punishing gamblers to helping them, and thus can be characterized as a positive, humane development. Yet the medical model has drawbacks too. The medical model can be used to justify paternalistic and potentially harmful interventions in the lives of individuals, and it obscures the social context in which individuals’ behaviour occurs. The drawbacks of the medical model can be illustrated with the example of gamblers who undergo personal bankruptcy proceedings. Many of the legal actors practicing bankruptcy law have adopted a medical approach to gamblers. They have reoriented their practices to serve therapeutic ends. Yet, they may be inadvertently harming the bankrupts they are trying to help. The risk of harms created by the medical model can be mitigated by educating legal actors about the social context in which gambling occurs. This article synthesizes research on the social context of gambling in Canada and illustrates how it can inform the practices of legal actors who implement Canadian personal bankruptcy law. The example of Canadian personal bankruptcy law underlines the importance of incorporating social context evidence into the practice law, especially when a legal issue has been medicalized.
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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 | 0.015 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.025 | 0.041 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".