A Participatory Approach to the Development of an Evaluation Framework: Process, Pitfalls, and Payoffs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.074 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it