SENTENCING OF PATHOLOGICAL GAMBLERS IN CANADA. LESSONS FOR SOUTH AFRICA?
Bibliographic record
Abstract
The focus of this note is on one specific type of sentencing, what is called “conditional sentence” in Canada, and “correctional supervision” in South Africa. The principles relating to this form of punishment in the two legal systems are again similar. It is a community-based punishment aimed at keeping the offender out of prison, and within society under strict conditions. The conditions in each case obviously depend on the circumstances of the case. In South Africa the measures mostly include house arrest, community service, monitoring and treatment. The choice of conditions in the Canadian legislation is wider, but includes a report to a supervisor, community service and attendance of a treatment programme (s 742.3 of the Canadian Criminal Code). The importance of this type of sentence for pathological gamblers is borne out by the fact that there is generally no need for these offenders to be removed from society. They are seldom violent, are susceptible to treatment outside the prison system and as such are capable of being rehabilitated. It is suggested that pathological gamblers in South Africa should, unless the seriousness of the crime demands otherwise, be sentenced to correctional supervision. This is the approach of the Canadian courts and should be the approach adopted by the South African courts.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".