Joint adoption of rice technologies among Bolivian farmers
Bibliographic record
Abstract
Abstract Bolivia has disseminated several improved technologies in the rice sector, but the average rice productivity in the country is far below the average trend in Latin America in recent years. Although the economic literature has highlighted the role of agricultural technology adoption in increasing agricultural productivity, gaps remain in understanding how rice growers are deciding to adopt and benefit from available improved rice technologies. Most previous adoption studies have evaluated the uptake of individual technologies without paying attention to the complementarities that alternative improved rice technologies may offer to farmers who face multiple marketing and production needs. This study uses data from a nationally representative sample of Bolivian rice growers to analyze farmers' joint decisions in adopting complementary agricultural technologies controlling for potential correlations across these decisions, as well as the extent of adoption of these practices. Evidence suggests that the decisions on multiple technology adoption are closely related, with common factors affecting both adoption and the extent of adoption. Furthermore, there is a need to better target resource-poor farmers, improve information-diffusion channels on agricultural practices, and better use existing farmers' organizations to enhance rice technology adoption.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".