States of Resistance: nosocomial and environmental approaches to antimicrobial resistance in Lebanon
Bibliographic record
Abstract
Drawing on institutional historical records, interviews and student theses, this article charts the intersection of hospital acquired illness, the emergence of antimicrobial resistance (AMR), environments of armed conflict, and larger questions of social governance in the specific case of the American University of Beirut Medical Center (AUBMC) in Lebanon. Taking a methodological cue from approaches in contemporary scientific work that understand non-clinical settings as a fundamental aspect of the history and development of AMR, we treat the hospital as not just nested in a set of social and environmental contexts, but frequently housing within itself elements of social and environmental history. AMR in Lebanon differs in important ways from the settings in which global protocols for infection control or rubrics for risk factor identification for resistant nosocomial outbreaks were originally generated. While such differences are all too often depicted as failures of low and middle-income countries (LMIC) to maintain universal standards, the historical question before us is quite the reverse: how have the putatively universal rubrics of AMR and hospital infection control failed to take account of social and environmental conditions that clearly matter deeply in the evolution and spread of resistance? Focusing on conditions of war as an organized chaos in which social, environmental and clinical factors shift dramatically, on the social and political topography of patient transfer, and on a missing "meso" level of AMR surveillance between the local and global settings, we show how a multisectoral One Health approach to AMR could be enriched by an answering multisectoral methodology in history, particularly one that unsettles a canonical focus on the story of AMR in the Euro-American context.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".