Optimizing Use in the Field of Program Evaluation by Integrating Learning from the Knowledge Field
Bibliographic record
Abstract
Abstract: It has been almost 20 years since Shulha and Cousins (1997) published their seminal paper exploring evaluation use. The paper examined a decade, 1986 to 1996, of theory, practice, and research on evaluation use. Since that time there have been significant developments related to the phenomenon of evaluation use. Outside of evaluation a new and burgeoning field has focused on the use of research in practice and policy; in health care the term knowledge translation has been used and in social sciences knowledge mobilization. Despite the rapidly growing body of research from the knowledge field, the different terminology used in evaluation, health care, and the social sciences has created siloed bodies of knowledge, even when working on similar change processes. This may be one of the factors why the large body of literature on evaluation use has received little attention in health care and vice versa. The aim of this article is threefold: first, to examine the developments in evaluation use since Shulha and Cousins’s (1997) paper; second, to explore how the knowledge fields, focusing on knowledge translation and mobilization, can help to further refine and develop our understanding of use; and third, to imagine what future research that interweaves the knowledge field with the field of program evaluation might look like and how it has the potential to serve the contexts where this research would be conducted.
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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 | 0.156 | 0.222 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.021 | 0.018 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.003 | 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".