“Fit for Purpose?” Assessing the Ecological Fit of the Social Institutions that Globally Govern Antimicrobial Resistance
Bibliographic record
Abstract
Antimicrobial resistance (AMR) is a natural process where microbes develop the ability to survive the antimicrobial drugs we depend upon to treat and prevent deadly infections, such as antibiotics. This microscopic evolution is further propelled by human activities, where each use of an antimicrobial drug potentially induces AMR. As microbes can spread quickly from animals to humans and travel around the world through humanity’s global circuits of movement, the use of any antimicrobial drug has potentially global consequences. As human-induced AMR occurs, mortality and morbidity increase due to increasingly or sometimes completely ineffective antimicrobial treatments. This article considers AMR as a product of the evolving and complex interplay between human societies and invisible microbial worlds. It argues that as a political challenge, AMR requires robust institutions that can manage human–microbial interactions to minimize the emergence of drug resistance and maximize the likelihood of achieving effective antimicrobial use for all. Yet, current governance systems for AMR are ill-equipped to meet these goals. We propose a conceptual paradigm shift for global AMR governance efforts, arguing that global governance could better address AMR if approached as a socioecological problem in need of sustainable management rather than solely as a medical problem to be solved. In biodiversity governance, institutions are designed to fit the biological features of the ecosystems that they are attempting to manage. We consider how a similar approach can improve global AMR governance. Employing the concept of ecological fit, which is defined as the alignment between human social systems and biological ecosystems, we diagnose 18 discrepancies between the social institutions that currently govern AMR and the ecological nature of this problem. Drawing from lessons learned in biodiversity governance, the article proposes five institutional design principles for improving the fit and effectiveness of global AMR governance.
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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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".