Understanding Evaluation Policy and Organizational Capacity for Evaluation: An Interview Study
Bibliographic record
Abstract
Evaluation policy has been identified as an important means of shaping and influencing organizational evaluation practice, yet, to date, little empirical research has been conducted to deepen our understanding of this relationship. The purpose of this study was to illuminate evaluation policy’s role in leveraging organizational capacity to do and use evaluation. We interviewed 18 published evaluation scholars and practitioners from North America and Europe about this topic. A thematic analysis of findings underscores the importance of context, policy attributes, enablers, and organizational benefits. Based on the findings, we developed an ecological conceptual framework to guide thinking about the role of evaluation policy in capacity building. We discuss these findings in terms of practical implications of understanding context, redressing the imbalance between learning and accountability purposes of evaluation, and organizational leadership, and we conclude with some implications for research.
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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 | 0.072 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".