Lina Britto. <i>Marijuana Boom: The Rise and Fall of Colombia’s First Drug Paradise</i>.
Bibliographic record
Abstract
While a great deal has been written about the history and politics of cocaine, scholarship on marijuana is scarce. Marijuana Boom: The Rise and Fall of Colombia’s First Drug Paradise is singular for what it offers. This is a fascinating, well-written, and much-needed book that brings to light an underexamined episode of Colombian history. Much more than this, the book weaves together a remarkable diversity of primary sources and grapples with multiple historiographic debates on subjects ranging from labor, immigration, and agrarian reform, to race, gender, and popular culture. Lina Britto’s case study of the marijuana boom of the 1970s in the Sierra Nevada de Santa Marta and Guajira Peninsula is set against the backdrop of much longer processes of capitalist expansion and state formation, from the early twentieth century to the Cold War and the war on drugs. Through extensive oral history interviewing and ethnographic observation, and the examination of local archival and news sources, song lyrics, fiction, as well as Colombian and US government correspondence, Britto shows how the marijuana business began, boomed, and then went bust. This book makes important contributions to conversations around the history of Colombia, but also more broadly commodities, drugs, and US foreign policy, as well as organized crime and popular culture. This book will also be of very special interest to oral historians and scholars of memory.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.076 | 0.013 |
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".