Evidence-Informed Policy-Making: Are We Doing Enough? Comment on "Examining and Contextualizing Approaches to Establish Policy Support Organizations – A Mixed Method Study"
Bibliographic record
Abstract
In their study of manifestations of policy support organizations (PSOs), Al Sabahi et al found that PSOs are united in their goal to support evidence-informed policy-making (EIPM), albeit with differing approaches. Their article is an important contribution to the body of research on evidence utilization and implementation. The unprecedented evidence climate presented by coronavirus disease 2019 (COVID-19) provides a unique window to motivate EIPM implementation. Research such as Al Sabahi and colleagues must prompt a dialogue regarding how best to address some of the current shortcomings in the field of EIPM. Monitoring and evaluation of best practices in EIPM is scarce. EIPM uptake is unsatisfactory, and the scientific community needs to ask itself why that is and what can be done. And, we should strive to develop a gradient that discerns between the convenient and the essential so countries can evaluate and pursue the policies to best address their greatest pain points through evidence.
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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 | 0.031 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.076 | 0.076 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".