Evidence-based policy making: determining what is evidence
Bibliographic record
Abstract
In the era of evidence-based policy, framing and assessing the core evidence is fundamental to our ability to use research in support of public policy. In a world of almost exponentially expanding scholarly publication, it is becoming harder to define what is known. This article reviews the basic theories of knowledge, the context for sorting through and summarizing that knowledge and a number of options available, and used, to assemble the knowledge base for research and policy work. The authors undertook a summative process in the domain of biotechnology, agriculture and development and offer insights into the comparative methods and their impacts on the outcome. A population sample of 421 articles was gathered. Four methods-expert Delphi, citation analysis, social network analysis and peer evaluation-were used to select the 51 pieces for inclusion and analysis in the core literature. That analysis shows that each process delivered a different set of evidence. The potential for bias in knowledge assessment can challenge policy makers in their process of reviewing evidence that rationalizes policy.
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 | 0.529 | 0.718 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.024 | 0.007 |
| Bibliometrics | 0.040 | 0.023 |
| Science and technology studies | 0.010 | 0.038 |
| Scholarly communication | 0.065 | 0.065 |
| Open science | 0.015 | 0.017 |
| Research integrity | 0.031 | 0.025 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".