Submission to the Intergovernmental Negotiating Body; RE: The need to include AMR-related provision in the UN/WHO Pandemic Instrument
Bibliographic record
Abstract
The Global Strategy Lab at York University offers this written submission in response to the current Working Draft of the pandemic instrument (document A/INB/2/3), developed in collaboration with our partners International Centre for Antimicrobial Resistance Solutions (ICARS), International Network for AMR Social Science (INAMRSS), One Health Trust (OHT), and ReAct – Action on Antibiotic Resistance. To enhance the pandemic instrument’s ability to protect the international community from future infectious disease crises, we recommend that the instrument be designed to comprehensively address the full range of pandemic threats. We note with concern that antimicrobial resistance (AMR) has not been adequately included within the scope of the treaty despite being a widely recognized and growing global pandemic.¨ In the submission we also give recommendations on how to include AMR.
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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 | 0.020 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.021 | 0.016 |
| Insufficient payload (model declined to judge) | 0.132 | 0.118 |
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".