Identities, interests, and preferences matter: Fostering sustainable community development by building assets and agency in western Kenya
Bibliographic record
Abstract
Abstract The efficiency and sustainability of climate change adaptation projects depend on appropriate models and tools to take climate‐smart practices to scale. This paper presents the “Building assets and agency” approach taken by the Accelerating Adoption of Agroforestry project whose objective is to scale the adoption of context‐specific adaptation and mitigation options. Through the approach, communities are encouraged to identify, mobilise, and use their existing assets to define community plans that are responsive to their identities, interests, and preferences. This innovative approach combines conscious selection of project staff and partners, group capacity and agency training, cocreation of skills in self‐selected agricultural practices with an emphasis on business skills, and tools for sustainable scaling through farmer‐to‐farmer extension. The paper addresses challenges and solutions, and case study data justifying proof of concept. While developed in a climate change context, and being sensitive to a number of factors, the approach can support effective, efficient, and socially appropriate action in any sector.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".