International negotiations for a pandemic treaty: A thematic evaluation of 43 member states
Bibliographic record
Abstract
Abstract Negotiating a successful pandemic treaty should address a four‐fold purpose: to prevent, prepare for, respond to and recover from pandemics effectively (Phelan and Carlson, 2022, Science , 377, 475). Since the proposal for an international treaty on pandemic prevention and preparedness was endorsed by 26 heads of state and the WHO Director‐General in March 2021, WHO member states assigned an Intergovernmental Negotiating Body (INB) with the responsibility of coordinating negotiations in anticipation of adopting an agreement by May 2024. To date, four INB meetings have been held. In this analysis, the interventions of 43 member states at the INB sessions in July 2022 were evaluated against components considered integral to an effective pandemic treaty. These were mapped against elements previously identified for a transformative treaty (Phelan and Carlson, 2022). This analysis outlines gaps in the current pandemic treaty working draft and the INB negotiations, which may identify opportunities to enhance the value of the remaining negotiations and guide refinement in developing further iterations of the working draft.
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 | 0.246 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".