Evaluation Utility Metrics (EUMs) in Reflective Practice
Bibliographic record
Abstract
Abstract: The article proposes three evaluation utility metrics to assist evaluators in evaluating the quality of their evaluation. After an overview of reflective practice in evaluation, the different ways in which evaluators can hold themselves accountable are discussed. It is argued that reflective practice requires evaluators to go beyond evaluation quality (i.e., technical quality and methodological rigor) when assessing evaluation practice to include an evaluation of evaluation utility (i.e., specific actions taken in response to evaluation recommendations). Three Evaluation Utility Metrics (EUMs) are proposed to evaluate utility: whether recommendations are considered (EUM c ), adopted (EUM a ), and (if adopted) the level of influence of recommendations (EUM li ). The authors then reflect on their experience in using the EUMs, noting the importance of managing expectations through negotiation to ensure that EUM data are collected and the need to consider contextual nuances (e.g., adoption and influence of recommendations are influenced by multiple factors beyond the control of the evaluators). Recommendations for increasing EUM rates by paying attention to the frequency and timing of recommendations are also shared. Results of implementing these EUMs in a real-world evaluation provide evidence of their potential value: practice tips led to an EUM c of 100% and and EUM a of over 80%. Methods for considering and applying all three EUMs together to facilitate practice improvement are also discussed.
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How this classification was reachedexpand
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 | 0.159 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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; both teacher heads 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".