Development of adaptive training materials for conservation agriculture promotion in Africa.
Bibliographic record
Abstract
<title>Abstract</title> In order for Conservation Agriculture (CA) to reach and impact small-scale farmers in Sub-Saharan Africa (SSA), CA technologies need to be adapted to suit the diversity of agroecological zones and cultures present on the continent. Training materials for CA promotion need to be similarly customizable to help extension staff and farmers develop their own, context-appropriate solutions from among the many possible CA approaches. From 2015 through 2018, a diverse set of farmer-level training materials for CA and complementary technologies was developed and field-tested by Canadian Foodgrains Bank partners. Together with a participatory, adaptive training methodology, these materials have enhanced the effectiveness of CA promotion, and they have been made available for copyright-free download in English, French, Kiswahili, Portuguese and Amharic (http://caguide.act-africa.org/, accessed 6 August 2021). This paper describes the process of developing these materials as well as challenges and constraints to their utilization.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".