Factors affecting multiple climate change adaptation practices of smallholder farmers in lower Eastern Kenya
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The study investigated the socioeconomic and institutional factors influencing uptake of multiple climate change adaptation practices among smallholder farmers in lower Eastern Kenya. Multistage sampling procedure was used to select 384 small-scale farmers. Percentage and regression were used in the analysis. Among the socio-economic factors, gender positively and significantly influenced adoption of conservation agriculture and water harvesting at 5%, respectively. Among the institutional factors, distance to markets positively or negatively influenced uptake of all the technologies at 1% and 5%, respectively. Due to complementarity in adoption of all the seven adaptation practices, age and distance to nearest markets should be considered during technology dissemination. The study, therefore, calls for agricultural policy reforms that aim at designing incentive programmes which adequately address most of the socioeconomic and institutional issues related to uptake of adaptation practices as well as encouraging off-farm diversification.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 it