Tracking global knowledge-to-policy pathways in the coronavirus crisis: A preliminary report from ongoing research
Bibliographic record
Abstract
In February 2020, when many parts of world were taking unprecedented measures to try to control the spread of the SARS CoV-2 coronavirus, INGSA turned to its global community for help to understand the evolving situation. As always, INGSA is especially interested in the evidence that lies behind the various policy decisions, and the pathways from evidence-to-policy.<br> Together with academic partners at the University of Auckland and the University of Sheffield, in collaboration with our IDRC-funded regional chapters, and with seed funding by the Fonds to de Recherche du Quebec and the World Universities Network, the INGSA executive and secretariat devised a mixed methods research project to examine the formulation of policy responses to the pandemic.<br> Phase 1 of this project comprised ‘citizen’-social-science that harness the enthusiasm, expertise and local knowledge of over 100 volunteer rapporteurs from across the INGSA network globally. With their help and commitment, we launched the INGSA Evidence-to-Policy Tracker. Volunteers used an online data-entry form to log information about the evidence and decision-making dynamics behind their countries’ key Covid policy responses.<br> The aim of this study is not to compare and assess the success of these interventions, but rather to compare the various ways in which evidence has been marshalled and applied. While there are now many useful trackers, INGSA uniquely complements these Covid policy trackers by seeking to unpack the formulation of evidence rather than to take it for granted, and to examine the evidence-to-policy pathways through this kind of tool.
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How this classification was reachedexpand
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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".