Parâmetros e paradigmas em meta-avaliação: uma revisão exploratória e reflexiva
Bibliographic record
Abstract
The evaluation of an evaluation is not a mere play on words or simply an investigation into the person who evaluates the evaluation. It is far more than that. It is about how to evaluate the diverse components of the evaluation process and the evaluators per se. In this paper, we discuss some promising possibilities for meta-evaluation in the field of evaluation of programs and services. These include fostering interaction between theoretical and practical production in fieldwork and supporting the definition of methods and strategies in a sector imbued with political interests and a profusion of methodological possibilities, and promoting ethical and scientific rigor in the evaluation practices. We conclude this exploratory and reflective review by raising some historical and political questions related to evaluation and meta-evaluation of programs and services and by criticizing the universalist and egalitarian pretensions contained in some evaluation approaches.
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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 | 0.408 | 0.447 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.033 | 0.032 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.034 | 0.053 |
| Open science | 0.009 | 0.014 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".