Methods development in evidence synthesis: a dialogue between science and society
Bibliographic record
Abstract
This chapter is about the science of evidence synthesis: the way that academics bring together knowledge from across multiple studies into a whole, to present the state of current understanding about a given area. While scientists have been doing this for centuries in the form of literature reviews, the advent of ‘evidence informed’ decision-making over the past 30-40 years has forced academics to develop a form of literature review that was demonstrably the sum of available knowledge in its area (rather than providing a partial and potentially biased picture). The key challenge methodologically has been in providing useful and useable evidence that can inform decisions, whilst not compromising on the high standards that usually need to be met to make claims about causality. Addressing this challenge has required the evolution of new research methods across multiple disciplines - something that seems likely to continue into the future.
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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 | 0.411 | 0.445 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.030 | 0.031 |
| Open science | 0.007 | 0.017 |
| Research integrity | 0.011 | 0.023 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".