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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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".