Lessons learnt from developing and applying research priorities during the COVID-19 pandemic: reflections from the Global Research Collaboration for Infectious Disease Preparedness (GloPID-R)
Bibliographic record
Abstract
⇒ In response to the COVID-19 pandemic, multiple activities to identify priority areas for research were undertaken.⇒ Existing guidelines for development of research priority agendas are limited in discussion of priority setting in the context of epidemic/pandemic response.⇒ We present key recommendation on best practice for developing and applying research priority agendas during outbreaks.⇒ These considerations represent key learnings from the COVID-19 pandemic, which can strengthen preparedness for responding to future epidemics and pandemics.
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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 | 0.297 | 0.306 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.042 |
| Scholarly communication | 0.040 | 0.037 |
| Open science | 0.012 | 0.045 |
| Research integrity | 0.062 | 0.120 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".