Syndemics in Symbiotic Cities: Pathogenic Policy and the Production of Health Inequity Across Borders
Bibliographic record
Abstract
Public health in border regions is a central concern to researchers and policymakers. This article demonstrates how and why syndemics theory should be central to border health research agendas and the development of health policy. A syndemic describes the concentration and deleterious interaction of two or more health conditions in a population. However, syndemic theory is not only about disease pathology. Another central tenant of syndemic theory is that the sociopolitical and environmental context facilitates the interaction of multiple health conditions. Within the social sciences and public health, a syndemics approach has become an increasingly utilized framework for understanding health disparities. However, how this framework can be adapted to understand the particularities of border regions remains underdeveloped. In applying syndemics to border regions, this paper explores how border-related policies produce conditions that facilitate syndemic vulnerability. In doing so, this article focuses on four policy realms as they unfold on the US-Mexico border: immigration policy, the War on Drugs, environmental policy, and health policy. The construction of policies within these realms often ignores the ways policies produced in one nation generate health consequences beyond national boundaries.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.024 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".