Community Based Participatory Monitoring and Evaluation: Impacts on Farmer Organization Functioning, Social Capital and Accountability
Bibliographic record
Abstract
Farmer organizations have taken root in the development agenda and practice in Sub-Saharan Africa. This is because they are recognized as a best-bet approach for achieving inclusive sustainable development. Group performance has, however, been varied - hence different mechanisms for improving group functioning have been developed, such as community driven Participatory Monitoring and Evaluation (PM&E). The effectiveness of community driven Participatory Monitoring and Evaluation in improving group functioning has not been rigorously evaluated. A study was therefore conducted to determine the impact of community driven Participatory Monitoring and Evaluation on group functioning using three Kenyan groups. Using a mixed methods approach, the study finds that farmer groups that integrated community driven Participatory Monitoring and Evaluation had higher indices for group social capital and performance. These groups exhibited greater group cohesion and members had higher satisfaction with group performance. Accountability, a key factor determining group functioning, was found to not differ significantly between groups with and without community driven Participatory Monitoring and Evaluation. Conclusions are that integrating community Participatory Monitoring and Evaluation in groups is essential for improving internal group functioning. However, this should be implemented in combination with other strategies that specifically aim to improve accountability. Without such an approach there is the danger of eroding the benefits of community driven Participatory Monitoring and Evaluation. Strategies to improve accountability must incorporate capacity building of group members' basic numeracy and literacy skills. This will enable the mostly illiterate membership to better understand and enforce accountability and, to better participate. Keywords: Kenya, community monitoring and evaluation, innovation, social capital, mixed methods
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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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".