[The role of science in policy making--EuSANH-ISA project, framework for science advice for health].
Bibliographic record
Abstract
Governments and other authorities (including MPs) should be well informed on issues of science and technology. This is particularly important in the era of evidence-based practice. This implies the need to get expert advice. The process by which scientific knowledge is transmitted, along with proposals how to solve the problem, is called science advice. The main aim of the article is to discuss the issue of science advice--definitions, interaction between science and policymaking, and its position in contemporary policies. The second aim is to present European Science Advisory Network for Health (EuSANH), EuSANH-ISA project, and framework for science advice for health which was developed by participants. Furthermore, the role of civil society in decision-making process and science advice is also discussed. Interaction between scientists and policy-makers are described in terms of science-push approach (technocratic model), policy-pull (decisionistic) and simultaneous push-pull approach (pragmatic). The position of science advice is described in historical perspective from the 50s, especially in the last two decades. Description relies to USA, Canada and UK. Principles of scientific advice to government (Government Office for Science, UK) are quoted. Some important documents related to science advice in EU and UN are mentioned. EuSANH network is described as well as EuSANH-ISA project, with its objectives and outcomes. According to findings of this project, the process of science advice for health should follow some steps: framing the issue to be covered; planning entire process leading to the conclusion; drafting the report; reviewing the report and revision; publishing report and assessing the impact on policy.
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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 | 0.059 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.022 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".