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UNGASS 2016: in de Weense houdgreep

2016· article· en· W2507218105 on OpenAlexaboutno aff
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Bibliographic record

VenueTijdschrift over Cultuur & Criminaliteit · 2016
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Systems and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical science

Abstract

fetched live from OpenAlex

UNGASS 2016: in the Viennese headlock This contribution aims to discuss the main outcomes of the recent UNGASS (United Nations General Assembly Special Session) on Drugs that took place in New York from 19 to 21 April 2016. Based on my own participation in the preparatory discussions and political negotiations as civil society representative (through the work of NGO Transnational Institute), I argue that political divisions and entrenched institutional dynamics have transformed what could have been the beginning of the end of the war on drugs into a wasted opportunity for changing the status quo of the present world regime regarding the production, trafficking and use of illegal drugs. Despite high initial expectations after several governments expressed a clear concern about the effects of purely repressive policies, and the UN decision to organize the session 3 years earlier than planned, very soon it was clear that the session would not imply real changes in the current policies. The agenda setting was non-transparent and controlled by the most conservative factions and countries, largely excluding the views from NGO’s and academics in the final adopted resolution. The final document poorly reflects the rich discussions and developments that are taking place in many countries of the world, particularly the debates and policy developments in ‘the Americas’. A positive note is that the unchanged international UN conventions on drugs can hardly cope with developments taking place on cannabis policies in countries such as Canada, Uruguay, United States or Jamaica. Also other countries are more and more prepared to push for change on other essential questions, including the application of death penalty for drug offences, the access to controlled medicines, or the explicit application of ‘harm reduction’ approaches.

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)0.005
metaresearch head score (Gemma)0.011
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.149
Threshold uncertainty score0.500

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0110.009
Open science0.0010.009
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.1490.062

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 head0.146
GPT teacher head0.495
Teacher spread0.349 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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