Analyzing antimicrobial resistance as a series of collective action problems
Bibliographic record
Abstract
Abstract Antimicrobial resistance (AMR) causes over 1.27 million deaths annually, making it one of today's most urgent health threats. Given its urgency, there are often calls for large‐scale global initiatives to address AMR. However, theories of collective action have yet to be applied to the problem in a systematic and holistic manner. Fuller engagement with collective action theory is necessary to avoid three risks, namely: mischaracterizing the kinds of challenges that AMR presents; over‐simplifying the problem by reducing it to a single type of collective action problem while ignoring others; and overstating the ability of collective action theory to formulate effective solutions. This article relies on the work of Elinor Ostrom to develop an analytical framework for collective action problems around public and common goods. When analyzed through this framework, we find that AMR poses at least nine distinct collective action problems. This more granular framing of AMR provides, in our view, a better basis to develop policy solutions to address this multifaceted challenge. We conclude with proposals for future research.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".