Bibliographic record
Abstract
Although combining methods is nothing new, more contributions about why and how to mix methods for validation purposes are needed. This article presents a case of validating the inferences drawn from the Participatory Evaluation Measurement Instrument, an instrument that purports to measure stakeholder participation in evaluation. Although the process was intended to be almost exclusively quantitative, one of its components unexpectedly turned into a mixed methods study. This, in turn, spurred on a cycle of instrument revision and further quantitative validation. Whereas the validation evidence is modest and tentative, it suggests that the revised version of the Participatory Evaluation Measurement Instrument offers a better fit with the respondents’ opinions regarding the participation level of selected evaluation cases. The article concludes with a brief discussion on the added value of mixed methods for validation purposes.
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 | 0.060 | 0.224 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.006 | 0.018 |
| Insufficient payload (model declined to judge) | 0.029 | 0.019 |
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".