Assessing the Practice Impact of Research on Evaluation
Bibliographic record
Abstract
Abstract Calls for more and better research on evaluation (RoE) have been sounded for some 20 years now and the recent appearance of several significant reviews of empirical research suggests that interest in RoE is on the rise. Although many empirical studies on evaluation and syntheses of such studies result in implications for evaluation policy and practice, to date there has been little attention devoted to tracking the practice impact of RoE. The chapter draws on recent work on research and knowledge utilization to develop a conceptual approach to assessing the impact of RoE on evaluation policy and practice. Specifically, a theory of change for RoE is developed as part of a contribution analysis (CA) approach to the problem. Recommendations for moving forward are then considered.
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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 | 0.549 | 0.814 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.023 | 0.026 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".