Expert Panels in Evaluation: An Update From the Field Using the DATA Model
Bibliographic record
Abstract
In this practice note, the authors reflect on the use and utility of expert panels in evaluation. They apply the describe, analyze, theorize, act model using interviews with evaluators, insights from peer-reviewed literature and their own professional observations. Connections are made to larger evaluation discourses regarding reflection, expert opinion, expertise, epistemic authority, and lived experience. It was found that expert panels are generally underutilized in evaluation due to a lack of awareness among evaluators as well as the perceived complexity associated with this method. However, the literature and interviews were clear that, when managed properly, expert panels can add tremendous value to an evaluation. There is therefore merit for more seriously considering panels in future evaluations. This note provides recommendations for evaluators and the evaluation community at large.
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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 | 0.300 | 0.251 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.018 |
| Science and technology studies | 0.006 | 0.035 |
| Scholarly communication | 0.024 | 0.036 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.014 | 0.020 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".