Criminal Policy of Netherlands and U.S.A on Decriminalization of Soft Drugs
Bibliographic record
Abstract
Background and Aim: Soft drugs are those drugs are very weak in terms of addiction and the effects are highly treatable. The challenging issue regarding this type of drugs is decriminalization of their use. Criminal policy of some countries such as the Netherlands agrees with the decriminalization and criminal policy of many others such as the United States of America is against it.Materials and Methods: In order to review the reasons and justifications of each of parties for and against soft drug decriminalization, ideas of lawyers and scientific studies in Netherland and the United States have been considered, and criminal poly makers’ ideas will be reviewed.Ethical Considerations: Honesty in the literature and citation analysis and reporting were considered.Findings: To justify soft drug criminalization, two classifications of legal and health reasons and justifications can be cited. Legally, it has been stated that drug issue is a problem related to health and treatment domain, and criminal legal intervention in this area must be considered just as the last weapon. In addition, Penologically, punishing soft drug users is unnecessary, ineffective, without favor and unnecessary. Regarding health, it has been stated that no only using some soft drugs such as cannabis is not the reason for being sick, but also it has an effective role in treating incurable diseases such as multiple sclerosis, AIDS, hepatitis, chronic pain and … .Conclusion: Decriminalization of soft drugs has a theoretical and practical background in countries such as the Netherlands and the United States. Soft drugs like cannabis not only have a lower degree of addictive rate rather than Hard drugs like heroin, but also it has some therapeutic benefits. Legally, decriminalization of soft drugs has root in human rights, penology and criminological justifications.Citation: Ehsanpour SR. Criminal Policy of Netherlands and U.S.A on Decriminalization of Soft Drugs. Bioeth Health Law J. 2017; 1(2):13-22.
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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 | 0.016 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".