Bibliographic record
Abstract
In a recent assessment of Campbell systematic reviews, we found that only about 10% of reviews published since 2017 had cited the previous Campbell searching guidance (Young et al., 2024).We hope that the updated version of the Campbell searching guidance will become a routine reference document for all Campbell authors moving forward.We also encourage authors new to conducting systematic review searches in the social sciences to take the Campbell Collaboration's online course on systematic reviews and metaanalysis (Unit 3 covers searching and is an excellent companion resource to the search guidance), which, as of the writing of this editorial, is freely available through the Open Learning Initiative (Valentine et al., 2022).With the support of these resources, and by involving a trained information specialist, researchers will be well equipped to produce thorough, robust, and transparent searches to support high-quality evidence synthesis and contribute to building a credible and trustworthy evidence base.
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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 | 0.208 | 0.511 |
| Meta-epidemiology (narrow) | 0.006 | 0.010 |
| Meta-epidemiology (broad) | 0.014 | 0.016 |
| Bibliometrics | 0.060 | 0.076 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.012 | 0.016 |
| Research integrity | 0.020 | 0.015 |
| Insufficient payload (model declined to judge) | 0.256 | 0.096 |
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".