From Prohibition to Decriminalization: Interrogating the Emerging International Paradigm Shift in the War on Drugs Discourse
Bibliographic record
Abstract
This project aims to interrogate the emerging counter-hegemonic war on drug discourse through a neo-Gramscian lens.Given that the estimated $2.5 trillion-dollar effort (Wood, 2011) has done little to stifle either drug consumption or production over the course of the past 40 years, a consensus is beginning to emerge surrounding the need for a policy change, especially in the countries of Latin America.While drug use was once something to be dealt with solely by the juridical and military branches of government, governments in the Americas increasingly understand drug use as a public health issue, including, although to a lesser extent, the United States.This thesis will focus specifically on the Bolivian and Guatemalan cases to ask if we are seeing the creation of a new hegemony in the War on Drugs context.
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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 | 0.013 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.077 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".