A machine learning approach to find the determinants of Peruvian coca illegal crops
Bibliographic record
Abstract
The current study analyzed the determinants of the Peruvian coca illegal plantations in the period 2003-2019. Hence, the DEVIDA database variables were gathered at first. Then, a machine learning-based technique is employed to select the most relevant variables for the study. That technique, Lasso, selected as accurate variables eradication of coca plantations and pasta base. Both OLS and VAR are employed to analyze the relevance of the selected variables. OLS finds that eradication was negatively related to the dependent variable. Nonetheless, pb confiscation had a positive relationship with illegal coca crops. Furthermore, VAR encounters that only pb confiscation affected the dependent variable. Supplementary tests are carried to ensure the accuracy of the results. In consequence, it is concluded that eradication policies by themselves were not enough to discourage the coca plantations. Farmers should get instruction about alternative crops and financial help. Furthermore, it has been claimed that pb confiscation generates scarcity of the drug, which elevates its price. Thus, coca farmers are more motivated to plant coca because of the higher prices. Therefore, as long as the international demand, which is disposed to pay high prices, the coca illegal crops and its illicit products will exist.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 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".