Improving Agricultural Productivity and Market Efficiency in Latin America and the Caribbean: How ICTs can make a Difference?
Bibliographic record
Abstract
Abstract: The rapid dissemination of ICTs in rural areas in LAC has been received with a lot of optimism, as these technologies are thought to be potentially effective tools of agricultural development. However, rigorous analyses of the impacts of ICTs on agriculture are still very scarce and lag behind the rapid penetration of these technologies. This paper is the first attempt to summarize recent findings from some of the few academic studies addressing this topic and complementing this analysis with anecdotal evidence and findings from case studies. Overall, the available evidence indicates that ICTs can play a major role in promoting agricultural productivity and rural development in LAC. By closing information gaps and reducing transaction costs, ICTs can improve the opportunities of farmers in agricultural markets and empower smallholders. ICTs can also foster productivity by facilitating the dissemination of technological knowledge and expand the access to financial and public services among the rural population by making service provision more affordable. Nonetheless, to the extent that the effective provision of ICTs has certain minimum requirements in terms of human and physical capital, many agricultural economies will be unable to reap the full benefits of these technologies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".