How and why consensus fractured at the 2024 session of the UN Commission on narcotic drugs: an exploratory study of international drug policy constellations using social network analysis and qualitative comparative analysis
Bibliographic record
Abstract
Background Consensus in international drug policy has fractured. It would be useful to explain how and why this occurred.Aim This exploratory study develops and tests theory and methods for describing and explaining constellations of policy actors and positions in international drug policy.Methods This article applies the policy constellations approach. It uses social network analysis (SNA) of the statements made by countries at the 2024 Commission on Narcotic Drugs, combined with a qualitative comparative analysis (QCA) of the data on countries’ value orientations and national levels of human development.Results A network analysis of the statements made at the Commission revealed two constellations of countries in the data: the ‘liberal’ and ‘traditionalist’ constellations. In QCA, after excluding Latin American countries, we find that a population’s level of emancipative values may have a causal effect on membership of these policy constellations; countries with high emancipative values are usually in the liberal constellation, and countries with low emancipative values are usually in the traditionalist constellation.Conclusion It is possible to use SNA and QCA to identify policy constellations in international drug policy discussions and to provide a provisional explanation of why countries (outside Latin America) adopt the policy positions they do.
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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 | 0.036 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".