How to Conduct a Metaevaluation?: A Metaevaluation Practice
Bibliographic record
Abstract
A metaevaluation is a quality cross-check to examine the conduct of an evaluation and validate the results. Of the few metaevaluation studies, almost none have reported on the metaevaluation procedure through a practical example evaluation. This study reports on the strengths and weaknesses of a program evaluation study in terms of the four main standards: utility, feasibility, propriety, and accuracy. It includes a metaevaluation process that involves both quantitative and qualitative analysis of data from eight meta-evaluators. It was found that while the evaluation study had very good utility and accuracy standards, the feasibility and propriety standards were only fair.
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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 | 0.736 | 0.857 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.017 | 0.023 |
| Bibliometrics | 0.024 | 0.015 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.019 | 0.021 |
| Open science | 0.010 | 0.008 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".