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Record W7135762271

The International Drug Control Regime: A Case for Regime Theory and Issue Salience

2020· dissertation· en· W7135762271 on OpenAlexaboutno aff
Eugenia Isabel Padilla Pineda

Bibliographic record

VenueDigital Repository (National Repository of Grey Literature) · 2020
Typedissertation
Languageen
FieldSocial Sciences
TopicCrime, Illicit Activities, and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsSalience (neuroscience)SalientControl (management)Human rightsInternational relationsInternational regimePsychological resilience
DOInot available

Abstract

fetched live from OpenAlex

Utilizing regime theory and the concept of issue salience, this study aims to show how the resilience of an agenda item can contribute to a change in an international security regime. The International Drug Control Regime's (IDCR) conventions and principles, rules and norms have been continuously contested over the past years. Several countries (Canada, Uruguay and some U.S. states) have moved forward with the legalisation of the recreational and medicinal use of cannabis, one of the drugs the regime has classified as 'highly dangerous', citing security, human rights and their citizen's preferences to explain their unilateral decision at the expense of the IDCR. The cannabis debate has become a salient issue in the IDCR, demonstrating patterns of change like internal contradictions, underlying structures of power and exogenous forces. This study will rely on a theoretical approach supported by a within-case historical analysis of the IDCR between 2009-2020, as well as discourse and documentary methods to assess how the salience of the cannabis debate to the regime's member-states can contribute to the possibility of a regime shift or change.

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)0.019
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0080.064
Scholarly communication0.0130.018
Open science0.0020.008
Research integrity0.0040.006
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 head0.007
GPT teacher head0.269
Teacher spread0.262 · 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 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

Citations0
Published2020
Admission routes1
Has abstractyes

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