The Promise of Science, Knowledge Mobilization, and Rapid Learning Systems for COVID-19 Recovery
Bibliographic record
Abstract
The health, economic, and social crises created by the coronavirus disease 2019 (COVID-19) pandemic have been global in scope and inequitable in impact. The global road to recovery can be enhanced with robust, relevant, and timely scientific evidence. This commentary seeks to illustrate the power of science, scientific collaboration, and innovative research funding programs to inform pandemic recovery and inspire transformational changes for a more equitable, resilient, and sustainable future. Specifically, this commentary provides an introduction to the United Nations (UN) Research Roadmap for the COVID-19 Recovery that was published in November 2020. It introduces 5 scoping reviews that helped inform the UN Research Roadmap and that are now available open access within this series of special papers, and it provides an overview of an innovative research funding program that facilitated rapid mobilization and collaboration to produce the scoping reviews. The publication of the scoping reviews in this journal series will help complement and amplify the UN Research Roadmap by furthering knowledge mobilization efforts and informing COVID-19 recovery around the world, to ensure a more equitable, resilient, and sustainable postpandemic future.
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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 | 0.076 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.009 | 0.030 |
| Scholarly communication | 0.026 | 0.039 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.015 | 0.016 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".