Estimating the distributional impact of innovation platforms on income of smallholder maize farmers in Nigeria
Bibliographic record
Abstract
This research studies the distributional effects of IP adoption on the farm income of smallholder maize farmers in Nigeria in an effort to move beyond the standard mean impact assessment of agricultural interventions. In order to account for selection bias that may result from both observed and unobserved factors, the study used a conditional instrumental variable quantile treatment effects (IV-QTE) strategy. The use of IPs greatly affects the revenue distributions of maize producers, as empirical evidence from the outcomes shows. Particularly, the impacts of adoption are stronger at the lower tails and just above the mean of the income distributions, indicating that impoverished farming households benefit more from the strategic functions of IP adoption in boosting income. These findings highlight how important it is to effectively target and disseminate improved agricultural technologies in order to increase the revenue of smallholder maize farmers in Nigeria from maize production. Agricultural research information and access to extension services are two policy tools that can help improve the successful adoption and diffusion of any agricultural intervention without favoring any particular groups.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".