Evidence synthesis, economics and public policy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Systematic reviews and syntheses of evidence are increasingly used to inform public policy decisions. Growing budgetary pressures mean that decision makers often need to consider evidence on the costs and efficiency of alternatives as well as their effects. There are a number of methodological challenges in the identification, appraisal, synthesis, interpretation and use of economic evidence. This article draws on a recently published edited volume to review the latest developments, proposals and controversies in these aspects of economic evidence synthesis methodology. It focuses on two broad classes of approach: systematic review to summarize and compare the findings of existing economic analyses and synthesis of new economic results using decision models. The availability and scope of economic evidence is currently limited in many fields, but improving. Increased engagement between economists, the wider evidence synthesis community, and decision makers is needed to improve both the production and use of economic evidence. Further research to improve the evidence base that underpins application of economic evidence synthesis methodology will need to embrace a broader range of methods than economic evaluation and systematic review alone. Copyright © 2010 John Wiley & Sons, Ltd.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 it