Gambling Addiction Defence on Trial: Canadian Expert Witness Perspectives
Bibliographic record
Abstract
The American Psychiatric Association’s evolving recognition of pathological gambling as a behavioral addiction (DSM-III, 1980; DSM-V, 2013) has occasioned increased use of the gambling addiction defence in criminal trials. Reflecting upon our experiences as expert witnesses in criminal and civil liability proceedings where gambling addiction was a significant factor, we a) describe the expert witness role; b) examine the links among frequent and intense EGM play, gambling addiction, and financially-based crimes; c) review how revisions to the Diagnostic and Statistical Manual influenced the Canadian judicial system response to such crime; and d) explore prospects for reducing criminal activity by addicted EGM players. We discuss how and why gambling addiction has become generally accepted as a mitigating factor in Canadian criminal trials. In this commentary we also analyze how the plight of addicted gamblers who resort to criminal behavior might be remediated by a) gambling-specific consumer protection measures; b) tighter regulatory control over the addictive elements of EGM play; c) the implementation of gambling courts; and d) a legislated duty of care owed by gambling providers to EGM players.
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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 | 0.034 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.029 | 0.022 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.009 | 0.007 |
| Research integrity | 0.025 | 0.024 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".