The urban political ecology of antimicrobial resistance: A critical lens on integrative governance
Bibliographic record
Abstract
The objective of this paper is to integrate Urban Political Ecology (UPE) as a theory for identifying under-exposed urban dimensions of Antimicrobial Resistance (AMR). A UPE lens allows us to conceptualize urbanization as a ubiquitous socio-ecological process and an interpretive frame that could inform AMR governance strategies across related contexts by: a) situating AMR risks in relation to urbanization processes shaping social and political co-determinants of such systemic threats as climate change; b) aligning UPE scholarship with One Health (OH) approaches that address AMR to reveal the under-exposed link of AMR to environmental threats and broader structural dimensions that influence these threats; and c) identifying shared AMR and environmental governance pathways that inform the rationale for more equitable governance arrangements. We delineate a context in which the speed and scale of human activity in the larger context of urbanization, driven by global market integration strategies, impacts human-animal-environmental health threats such as AMR. We demonstrate how UPE scholarship can be leveraged to offer theoretical depth to approaches considering the interdependencies of AMR and climate change threats. We then propose a strategic approach focused on identifying shared governance pathways and intersectoral accountability frameworks to address upstream structural drivers of AM-Environmental threats. The co-benefits of a UPE-informed framework to human-animal-environmental health that leverages enabling policy environments to foster a more collaborative, equitable and sustainable approach to address systemic global health threats are clarified. Just as the concept of "health in all policies" emphasized taking health implications into account in all public policy development, the integration of UPE in AMR governance arrangements would emphasize the need to take other sectors into account through an intersectoral whole-of-government approach that fosters shared AMR - climate change governance pathways.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.068 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".