Local support for the US–Mexico border wall and local immigration policy
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
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.
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.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 it