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Record W7047602034

High Time for a Change: How the Relationship Between Signatory Countries and the United Nations Conventions Governing Narcotic Drugs Must Adapt to Foster a Global Shift in Cannabis Law

2021· article· en· W7047602034 on OpenAlexaboutno aff

Bibliographic record

VenueeYLS (Yale Law School) · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSuperconducting and THz Device Technology
Canadian institutionsnot available
Fundersnot available
KeywordsCannabisCriminalizationDecriminalizationTreatyImprisonmentLegislationConventionEffects of cannabisGlobe
DOInot available

Abstract

fetched live from OpenAlex

Since the early 1970’s, the inclusion of cannabis and its byproducts in the United Nations Single Convention on Narcotic Drugs has mandated a strict prohibition on cultivation and use of the substance, which has led to a largely global practice of criminalization and imprisonment of anyone found to be in its possession. Yet recently, mostly in response to growing public health concerns, countries like Uruguay, Portugal, The Netherlands, Canada, and the United States have enacted laws which seek to decriminalize or even legalize cannabis use and possession. Yet, cannabis remains classified as a Schedule IV narcotic under the Single Convention, a categorization reserved for only the most dangerous of drugs. This article traces the history of cannabis’ inclusion in the international treaties that collectively establish the framework under which cannabis currently regulated across the globe and outlines how the cannabis policies of the aforementioned countries have been enacted in spite of those treaties. The article then analyzes proposed methods of amending the current Conventions, while ultimately suggesting a policy of renunciation and re-accession as the most suitable method for maintaining international treaty compliance in the face of a global rise in efforts to legalize and decriminalize recreational cannabis use.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.676
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.031
GPT teacher head0.267
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

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