Bibliographic record
Abstract
Systematic reviews are increasingly seen as helpful "knowledge support" for managers and policy makers, and deliberative processes are starting to be seen as promising, locally contextualized "decision support." Increases to the flow of systematic reviews should be complemented by efforts to facilitate the retrieval, and adapt the presentation, of the available stock of systematic reviews. Research and other evidence should be combined in transparent ways to facilitate cross-context learning. The challenge for managers and policy makers in moving forward will be to avoid the confusion that comes from the branding of both systematic reviews and deliberative processes.
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 | 0.508 | 0.498 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.013 | 0.010 |
| Bibliometrics | 0.019 | 0.012 |
| Science and technology studies | 0.008 | 0.076 |
| Scholarly communication | 0.043 | 0.108 |
| Open science | 0.014 | 0.060 |
| Research integrity | 0.048 | 0.077 |
| Insufficient payload (model declined to judge) | 0.016 | 0.010 |
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".