harm reduction policy from perspective of jurisprudence principles
Bibliographic record
Abstract
Public protection has become a key theme of much recent criminal justice legislation and policy aimed at the effective management of high-risk offenders. Harm reduction policy during more than three decades in the world to reduce the risk of abnormal behavior.Harm reduction is a health-centered approach that seeks to reduce the health and social harms associated with drug use, without necessarily requiring that users abstain. Harm reduction is a non-judgmental response that meets users “where they are” with regard to their substance use rather than imposing a moralistic judgment on their behaviors. As such, the approach includes a broad continuum of responses, from those that promote safer substance use, to those that promote abstinence.This article by descriptive-analytical method, the first describe implementation of harm reduction policy according to successful policies ofPortugal and Canada.The secondthis research comparisonharm reduction policy between Iran and those two government.Thispaperproposes"bill ofdecriminalizing fromdrug lawandtreatment ofdrug abuse," andits amended should be put on the agenda. Firstly, policy-makers should decriminalization touseofsomelow-riskdrugs because relevant authorities canwithaction freedomto implementharm reductionpolicy, and secondly, the behavior ofalldrug users should be diversion toaccess todrug user. Thirdly,age of drug users descend thuspolicy-makers should be consideryouthinharm reduction programs.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.030 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".