PARTICIPATORY RESEARCH METHODS FOR TECHNOLOGY EVALUATION: A MANUAL FOR SCIENTISTS WORKING WITH FARMERS
Bibliographic record
Abstract
This manual presents methods that enable agricultural scientist and farmers to evaluate technologies/practices jointly. The methods are specifically designed for participatory research on germplasm and soil fertility technologies, and they are illustrated with actual examples from three research projects. The manual begins by reviewing conceptual issues that are important in participatory research and presents information to assist researchers in selecting research sites and fieldwork participants. Next, the manual describes the rationale and associated methods for each major activity in farmer participatory research: diagnosing farmers' conditions, evaluating current and new technologies/practices, and assessing their impact. Goals, procedures, advantages, and limitations of each method are outlined. The manual also presents detailed information on analyzing data gathered through participatory methods, discusses differences between gathering data through participatory methods and more traditional structured farm surveys, and offers examples, based on field experience, of the choices and strategies involved in applying these methods.
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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 | 0.077 | 0.077 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.084 | 0.061 |
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".