Local support for the US–Mexico border wall and local immigration policy
Bibliographic record
Abstract
A signature policy of former US President Donald Trump was his plan to halt unauthorized migration from Mexico by building a wall the length of the US–Mexico border. While the existing research has identified several political, demographic and spatial correlates of individual-level support for (or opposition to) the wall, existing research has yet to provide local-level estimates of aggregate support for a border wall and an account of its spatial distribution. Using multilevel regression and synthetic poststratification (MrsP) and data from large-scale public opinion surveys conducted between 2016 and 2022, this article presents county-level estimates for support for the US–Mexico border wall. The results demonstrate that while a majority of the American public opposes the construction of the wall, there is substantial variation in county-level support. Support for the wall is highest in areas where Trump received strong support in the 2016 and 2020 presidential elections. Support is also linked to proximity to the US–Mexico border and racial–ethnic composition at the county level in complex ways. It is similarly linked to county-level cooperation (or lack thereof) with federal immigration enforcement, pointing to an opinion–policy link at the local level.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".