Achieving equity to fully realise the pandemic agreement
Bibliographic record
Abstract
The Pandemic Agreement’s (PA’s) unanimous adoption by the 78th World Health Assembly is an important step in preparing domestic and international health systems for future pandemics. As the PA undergoes ratification by individual member states, and the Intergovernmental Working Group prepares the Annex on Pathogen Access and Benefit Sharing that includes vaccines and therapeutics, it is imperative that it is implemented with fidelity to its stated values. As ethicists specialising in public health and global health, we applaud the centrality that equity, solidarity and trust enjoy in the PA. At the same time, we caution that without explicitly naming the injustices that lead to health inequities, like colonialism, efforts to build trust and solidarity to achieve equity will fail. We fully acknowledge that the negotiations for the PA were contentious and that its adoption was likely based, in part, on intentionally overlooking historical injustices. Despite—or perhaps in spite—of that ahistorical compromise, recognising the root causes of global health inequity will be critical for the PA’s success so that it does not merely serve the existing unjust status quo. Now that the PA has been adopted, we highlight how this can be achieved in the interpretation and implementation of the PA’s own values.
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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 | 0.034 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.038 | 0.007 |
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".