Bibliographic record
Abstract
This study provides up-to-date scientific estimates of annual revenues generated by Colombia's illicit cocaine industry (1991-2007), imputed from data on coca production collected by the United Nations Office on Drugs and Crime. While Colombian producers appropriate only a fraction of global revenues from cocaine trafficking and sales, control over production and appropriation of revenues is highly concentrated, suggesting a great capacity for illegal drug-firms to impact Colombian economy and society. We compare narco-capital accumulation within the wider context of the Colombian economy in terms of productivity, employment patterns, growth and concentration of wealth and power and find that narco-production ranks among the most productive and lucrative sectors of the economy. But while the potential for profits is high, the illegal nature of the industry means firms are prone to sabotage and violence from competitors and vulnerable to attempts at suppression of production by the state, making the industry highly volatile and risky. If illegally accumulated drug-money can serve as a source of financing for legal economic activities, thus propping up economic growth in the formal sector, it must also be said that illegally accumulated narco-dollars are used to finance illegal armed groups and contribute to violence and insecurity, particularly for rural peasants.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".