Bibliographic record
Abstract
Abstract: In this article, we discuss the importance of communicating the evaluation approach (process, methodology, results, and limits) to promote the use of results and the implementation of recommendations. We present an education-focused, meta-evaluative training tool based on the methodological aspects of the evaluation process, and designed to support evaluators, particularly novice evaluators, in the rigorous planning, implementation and communication of methodology. We are focusing on communicating the program evaluation approach though evaluation reports (technical and final) that are usually the means offering the most information on both results and the evaluation process. We realize that there are other means of communication (i.e., journal-published articles), but their format doesn’t always allow the provision of all the information relevant to results and the approach chosen by evaluators, since they usually only present highlights and not methodological aspects. Since we zeroed in on methodological considerations, it seemed to us that it would be more relevant to base ourselves on the information included in reports. This is not an innocuous choice, as an evaluation’s quality depends, in part, on a recognized methodology that is able to provide the solid evidence needed to exercise judgment. Also, we discuss the role of meta-evaluation (MEV) in enriching evaluation practice and promoting program evaluation’s quality. Furthermore, we present a meta-evaluative tool we designed to encourage the operationalization of quality evaluative methodologies and the implementation of effective evaluative practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.418 | 0.627 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.016 | 0.009 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.020 | 0.018 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
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".