From private incentives to public health need: rethinking research and development for pandemic preparedness
Bibliographic record
Abstract
Pandemic preparedness and response have relied primarily on market dynamics to drive development and availability of new health products. Building on calls for transformation, we propose a new value proposition that instead prioritises equity from the research and development (R&D) stage and that strengthens capacity to control outbreaks when and where they occur. Key elements include regional R&D hubs free to adapt well established technology platforms, and independent clinical trials networks working with researchers, regulators, and health authorities to better study questions of comparative benefit and real-world efficacy. Realising these changes requires a shift in emphasis: from pandemic response to outbreak control, from one-size-fits-all economies of scale to R&D and manufacture for local need, from de novo product development to last-mile innovation through adaptation of existing technologies, and from proprietary, competitive R&D to open science and financing for the common good that supports collective management and sharing of technology and know-how.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.014 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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".