A Participatory Approach to the Development of an Evaluation Framework: Process, Pitfalls, and Payoffs
Bibliographic record
Abstract
Abstract: Much literature exists on participatory approaches to developing and implementing program evaluation. Little is documented, however, about participatory approaches to developing an evaluation framework. This article reports a case study of implementation of a participatory evaluation approach and examines the results in light of participatory evaluation theory. A participatory approach was used to develop a provincial evaluation framework for a unique, collaborative community/provincial/federal funding program for community-based HIV/AIDS service organizations in Alberta, Canada. The participatory process resulted in significant capacity building, mutual learning, and relationship development, as well as a comprehensive and user-friendly provincial evaluation framework. The purpose of this article is to share our process, the pitfalls, and the payoffs to our participatory approach in developing an evaluation framework.
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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 | 0.425 | 0.267 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.016 | 0.034 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".