Eficiencia en la producción de quinua en Bolivia: Un análisis de Fronteras Estocásticas
Bibliographic record
Abstract
The article aims to evaluate the efficiency of quinoa production in Bolivia, identifying factors that influence production variability and the sustainability of the crop. The methodology employed includes a stochastic frontier analysis to measure producers' technical efficiency, as well as the relationship between inputs and production under different climatic conditions. The study's conclusions highlight the heterogeneity in production levels associated with producers' capacity for capital and operating investment, as well as their access to sustainable agricultural technologies. Moreover, labor does not always result in a proportional increase in production, suggesting the need to improve labor management. The identified levels of inefficiency are linked to proxy variables of sustainable production systems, which calls for policies that promote more modern agricultural technologies. Finally, a wide margin for efficiency improvement is identified, underscoring the importance of investing in training and technology. These recommendations aim not only to benefit producers but also to strengthen the crop's resilience and ensure the long-term viability of quinoa production in Bolivia.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".