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Record W4206026375 · doi:10.1079/9781789245745.0023

Development of adaptive training materials for conservation agriculture promotion in Africa.

2022· book-chapter· en· W4206026375 on OpenAlexaboutno aff
Neil W. Miller, Putso Nyathi, Jean Twilingiyumukiza

Bibliographic record

VenueCABI eBooks · 2022
Typebook-chapter
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Rural Development Research
Canadian institutionsnot available
Fundersnot available
KeywordsPromotion (chess)AgricultureContext (archaeology)Training (meteorology)Citizen journalismBusinessScale (ratio)AmharicPortugueseConservation agriculturePolitical scienceEngineeringGeographyComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

<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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.967
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.109
GPT teacher head0.243
Teacher spread0.134 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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