Mortality, Surveillance and the Tertiary “Funnel Effect” on the U.S.-Mexico Border: A Geospatial Modeling of the Geography of Deterrence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Theories of migration deterrence have long posited that border enforcement infrastructure pushes migration routes into more rugged and deadly terrain, driving an increase in migrant mortality. Applying geospatial analysis of landscape and human variables in one highly-trafficked corridor of the Arizona / Sonora border, we test whether the expansion of surveillance infrastructure has in fact shifted migrants’ routes toward areas that are more remote and difficult to traverse. We deploy a modeling methodology, typically used in archaeological and military science, to measure the energy expenditure of persons traversing the borderlands. Outcomes of this model are then compared to the changes in border infrastructure and records of fatality locations. Findings show that there is a significant correlation between the location of border surveillance technology, the routes taken by migrants, and the locations of recovered human remains in the southern Arizona desert. Placed in the context of ongoing efforts by the United States to geographically expand and concentrate border surveillance and enforcement infrastructure, we argue that this suggests a third “funnel effect” that has the outcome of maximizing the physiological toll imposed by the landscape on unauthorized migrants, long after migration routes have moved away from traditional urban crossing areas.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it