Reporting guidelines for realist evaluations seek to improve clarity and transparency
Bibliographic record
Abstract
An increasing number of realist evaluations are being conducted from a wide range of disciplinary perspectives and with diverse, fit-for-purpose methods. This commentary discusses the recent BMC Medicine publication of RAMESES II reporting guidelines for realist evaluations. Knowledge users such as program implementers and decision-makers will benefit from the increased transparency of reporting and interpretation in light of the totality of evidence encouraged by this guidance. It is hoped that these reporting guidelines will eventually lead to improved knowledge synthesis and contribute to the cumulative science regarding realist evaluation.Please see related article: https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-016-0643-1.
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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 | 0.625 | 0.853 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.019 | 0.019 |
| Open science | 0.014 | 0.011 |
| Research integrity | 0.045 | 0.057 |
| Insufficient payload (model declined to judge) | 0.005 | 0.008 |
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".