A machine learning approach to find the determinants of Peruvian cocaine local price
Bibliographic record
Abstract
The coca leaf has many uses in the Peruvian culture. Although there are legal usages, people employ coca for illicit business. The most infamous illegal use is cocaine production. The cocaine business is highly profitable, but it harms human health. Then, what are the determinants of cocaine price? The current analysis aims to get the variables with the capability to explain the cocaine prices in Peru. The period analyzed is 2003-2019. The study gathered variables from DEVIDA and UNDOC databases. The Lasso technique selected the variables with the best capability to predict cocaine price. Those variables were: ENACO acquisition, coca seizures, and coca crops. OLS, VAR, and Granger analyses employed those variables to analyze the relationship between them. According to the OLS analysis, both ENACO acquisition and coca crops had adverse effects on cocaine prices, while coca seizures were positively related to the cocaine price. VAR analysis showed that only ENACO acquisition had a short-term relationship with the dependent variable. Moreover, it showed that the whole set of variables influenced the dependent variable. The Granger analysis proved that there was a cause-effect relationship between ENACO acquisition and cocaine price. Hence, the ENACO purchases expansion can rest the attractiveness of illegal groups to farmers. However, lowering cocaine prices might attract more users. Therefore, educational activities are also required.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".