EVALUATING PARTICIPATORY ACTION RESEARCH: THE IDRC/SPARC/GES/FUDECO RESEARCH ON PASTORALISTS WOMEN IN MAYO INNE DISTRICT, FUFORE LOCAL GOVERNMENT AREA, ADAMAWA STATE, NIGERIA
Bibliographic record
Abstract
In 2022 and 2023, Fulbe Development and Cultural Organisation (FUDECO) conducted participatory action research (PAR) in a pastoralists’ settlement in Mayo Inne District of Fufore Local Government Area of Adamawa State, Nigeria. The research was on behalf of the International Development Research Center, Canada (IDRC), Supporting Pastoralism and Agriculture in Recurrent and Protracted Crisis (SPARC), and Gender Equality in the Sahel (GES). The research highlighted part of the growing interest in participatory action research (PAR). However, despite this interest and the vast literature on evaluation work generally, little is known about the evaluation of participatory action research (PARs). The overall trajectories that emerge are very detailed explanations of how to evaluate other forms of research especially those generating primary data through questionnaires and laboratory tests. The PAR in Mayo Inne was not one of them. This article reviews basic evaluation literature while drawing attention to the fact that such literature has failed to provide techniques, ways, or approaches to how PARs can be evaluated. Therefore, using the Mayo Inne example, the article explains how the research was evaluated without using traditional evaluation methods and proposes that the method used for the Mayo Inne research would also be valuable in evaluating PARs in other places and therefore will contribute to the central tenets of evaluation.
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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 | 0.069 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.012 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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 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".