Participatory Evaluation in the Context of CBPD: Theory and Practice in International Development
Bibliographic record
Abstract
Abstract: This article reviews current trends in community-based participatory evaluation (CBPE) and presents an overview of related evaluation tools. These approaches have been widely implemented in an international arena, including Canada and the United States and the developing world. The theoretical approach guiding this article stems from current trends in international development thinking. The author argues that participatory evaluation is the most effective means of assessing community-based development initiatives. A comparative examination of three evaluation methodologies, however, reveals that not all those claiming to support the central tenets of CBPE actually promote democratic participation. This article reflects a growing international interest in CBPE and Canada’s participation in development efforts of this nature, both locally and in the global South.
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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 | 0.184 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.012 | 0.053 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".