1. THE EVIDENCE AGENDA – 15 Chapter 1 The Evidence Agenda
Bibliographic record
Abstract
In this chapter, we examine the resurgence on interest in “evidence”, outline the roles and relationships between major stakeholders, and provide an overview of this publication. Part One: Setting the Stage: The Evidence Agenda and Methodological Issues In recent years a number of public crises have seized the attention of the world and required rapid responses from governments to ensure the health and safety of the public and maintain their confidence in policy makers. The 2001 UK foot and mouth crisis and the emergence of SARS in Asia and Canada highlight the difficulties of decision-making for policy makers and also the necessity for time-sensitive information on which to base those decisions. In each of the above examples, dramatic action needed to be taken urgently. These decisions resulted in substantial economic and societal losses, as well as worldwide reaction to piles of scorched carcasses, delayed elections, passengers wearing masks while taking public transport, and restricted movement. The dangers were contained and the emergencies passed, but post factum evaluations revealed that perhaps the policy decisions taken were not, in fact, the most effective or efficient ones available
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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 | 0.289 | 0.497 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.040 | 0.039 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.038 | 0.038 |
| Insufficient payload (model declined to judge) | 0.017 | 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".