Introduction: AMR Belongs in the Pandemic Instrument
Bibliographic record
Abstract
In the wake of COVID-19, the World Health Organization established an Intergovernmental Negotiating Body to negotiate a new instrument for pandemic prevention, preparedness, and response. This special issue of the Journal of Law, Medicine & Ethics brings together multidisciplinary scholarship to address the question of whether antimicrobial resistance should be included in this new instrument. Drawing from disciplines including law, anthropology, history, public health, public policy, economics, and veterinary medicine, this special issue explores the inclusion of AMR within the Pandemic Instrument from three perspectives: first, through the lens of global AMR governance, second, from the perspective of technical governance challenges and opportunities affecting the global ability to address AMR and future pandemics, and third, from the perspective of pandemic instrument mechanisms for strengthening global AMR governance. Each paper makes a concrete recommendation with respect to the importance of including AMR within the scope of the pandemic instrument.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.015 | 0.012 |
| Insufficient payload (model declined to judge) | 0.014 | 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; 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".