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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".