An Assessment of the Theoretical Underpinnings of Practical Participatory Evaluation
Bibliographic record
Abstract
This article is concerned with the underpinnings of practical participatory evaluation (PPE). Evaluation approaches have long been criticized because their results are often not used. It is believed that PPE addresses this drawback. The article focuses on the mechanisms underlying the links between activities and consequences in PPE. A PPE theory is proposed, based on learning theories and knowledge transfer theories, which comprises four key concepts and three hypotheses. The key concepts are interactive data production, knowledge coconstruction, local context of action, and instrumental use. The hypotheses articulate the relationships between these concepts. The article provides theoretical and empirical evidence to support the hypotheses discussed and present a framework for the proposed PPE theory. The importance of practitioner knowledge and participation in the PPE process in enhancing the use of results is partially supported by the literature. In general, it seems that the support is more theoretical than empirical.
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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 | 0.111 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.005 | 0.031 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".