MétaCan
Menu
Back to cohort
Record W4388420150 · doi:10.1002/ijgo.15214

Maternal health and human rights impacts of Russian drug policy

2023· article· en· W4388420150 on OpenAlexaff
Mikhail Golichenko, Sandra Ka Hon Chu, Renée Lehman

Bibliographic record

VenueInternational Journal of Gynecology & Obstetrics · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicSex work and related issues
Canadian institutionsMcGill UniversityHIV Legal Network
Fundersnot available
KeywordsCriminalizationHuman rightsConvention on the Elimination of All Forms of Discrimination Against WomenPunitive damagesMedicineConventionRight to healthLawHealth careConvention on the Rights of the ChildCriminologyInternational human rights lawPolitical scienceSociology

Abstract

fetched live from OpenAlex

This article describes how Russian drug policy defies international ethical standards in patient care and violates the human rights of pregnant people who use drugs. While the CEDAW Committee previously found Russia to be in violation of the Convention on the Elimination of All Forms of Discrimination against Women (CEDAW) by failing to ensure that pregnant people have access to gender-sensitive drug dependence treatment, to date the Committee has refused to address the role of drug criminalization in enabling this human rights violation. This article outlines the gendered impacts of Russia's punitive approach to drug use, including its detrimental effects on maternal health, and concludes by urging the CEDAW Committee to follow the approach of the UN Committee on Economic, Social and Cultural Rights, the UN Chief Executives, the World Health Organization, and UNAIDS, as well as senior UN lawyers and international legal experts to assess drug criminalization critically through the prism of the CEDAW convention.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.005
Scholarly communication0.0030.001
Open science0.0000.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.016
GPT teacher head0.363
Teacher spread0.347 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations2
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Gynecology & ObstetricsSame topicSex work and related issuesFrench-language works237,207