Editorial for the Thematic Series in Agriculture & Food Security: Climate-Smart Agriculture Technologies in West Africa: learning from the ground AR4D experiences
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
This Thematic Series on “Climate-Smart Agriculture \nTechnologies in West Africa: learning from the ground \nAR4D experiences” contains seven papers presented by \nresearchers from four West African countries based on \nparticipatory action research conducted since 2012 in \nthe region. These research activities were funded by the \nCGIAR Research Program on Climate Change Agriculture \nand Food Security (CCAFS) through a project titled \n“Developing community-based climate-smart agriculture \nthrough participatory action research in CCAFS benchmark \nsites in West Africa” (see [1]). This research action \nunder the scientific lead of the World Agroforestry Centre \n(ICRAF) aimed to test and validate, in partnership \nwith rural communities and other stakeholders, scalable \nclimate-smart village models for agricultural development \nthat integrate a range of innovative agricultural risk \nmanagement strategies. The project also aimed to enable \nfarmers, developers, managers and policy makers for the \nagriculture sector to develop cost-effective climate-smart \nagriculture (CSA) options that support local sustainable \ndevelopment and enhance livelihood resilience. It is \ntherefore a response to the challenges (degraded lands, \nlow crop productivity, high level of poverty for rural people, \netc.) faced to satisfy the food needs of an increasing \npopulation in the face of a changing climate...
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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