Bibliographic record
Abstract
Summary: This report summarises the key activities, findings and outputs from the Antimicrobials in Society (AMIS) programme (2017-2021). This final report highlights our work on the three key commitments of the AMIS programme: research, stakeholder engagement and promoting fresh approaches to AMR. Background: Antimicrobial resistance (AMR) is a potentially catastrophic global problem. Our use of antimicrobial drugs, including antibiotics, has escalated. These medicines are now a routine part of everyday life. For example, we use antibiotics not only to cure infections but in anticipation of infection for people, animals, and crops. We propose that the ways antibiotics are used is deeply embedded in the ways our societies and economies work. It is important to understand the extent and nature of the way we have become intertwined with these medicines in order to understand the consequences of resistance and the best ways to reduce it as a threat. Aims: The AMIS programme promoted fresh approaches to the study of antimicrobials in society. The AMIS co-investigators – from the UK, Thailand and Uganda – aimed to explicate the rich social material worlds that antimicrobials inhabit and travel within, and in doing so offer policy-makers, scientists, and funders new ways to conceptualise and act upon AMR. Work strands: The AMIS programme ran from April 2017 to July 2021 and comprised two parallel work strands – empirical research and dissemination, and the AMIS Hub. RESEARCH AND DISSEMINATION: Drawing on conceptual and methodological tools primarily from anthropology, but in conversation with other disciplines, the AMIS research projects in Thailand and Uganda carried out a series of case studies that traced out the multiple roles that antimicrobials take in society today, and how they enable everyday life. Each case study engaged with different stakeholders throughout the project, with dissemination of findings a core objective. AMIS HUB: our website, newsletter, events and social media activity aimed to promote fresh approaches in social research on AMR. Our primary audience was the AMR community, comprising other social scientists, other researchers, funders, policy makers and practitioners. Final report: The report describes the AMIS programme structure, the research and findings, the AMIS Hub activities and the outputs of the programmes including written, video and other materials.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.442 | 0.325 |
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".