Participatory evaluation for development: Examining research-based knowledge from within the African context
Bibliographic record
Abstract
Background: Participatory and collaborative approaches to evaluation have grown in popularity in recent years, as program contexts increasingly require more culturally responsive and inclusive approaches to addressing complex community, program and organisational needs.This is particularly the case in development evaluation contexts such as Africa. We recently conducted a systematic review and integration of the literature on participatory evaluation that included the review of 121 empirical studies published in peer-reviewed journals and other outlets (Cousins & Chouinard 2012). In that review, only 21 studies derived from development contexts and, of those, only six from Africa.Objectives: In this article, we considered the applicability and relevance of the thematic discussion by Cousins & Chouinard (2012) to the African development context through a close-up look at research in Africa on participatory evaluation.Method: We carefully examined the African studies and, through a conceptual critique, re-examined the prior thematic analysis.Results: We observed that some themes did not give primacy to context and relationships which are essential considerations in the African context. Further, an emphasis on empowerment-oriented outcomes begs attention to societal, cultural and economic considerations, implication for evaluators’ roles and a deeper understanding of power issues.Conclusion: We concluded that our thematic discussion did not resonate well with participatory evaluation in development contexts and that a much more focused and targeted review and integration of research was warranted.
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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 | 0.289 | 0.278 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.021 | 0.016 |
| Science and technology studies | 0.010 | 0.032 |
| Scholarly communication | 0.020 | 0.024 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".