Presidential Voting in the 2016 US Presidential Election: Impacts of the US–Mexico Border and Border Integration
Bibliographic record
Abstract
The 2016 presidential election brought many proposals to the fore, several with potentially significant impacts in the US–Mexico border region. Republican candidate, Donald Trump, promised to build a border wall, return manufacturing jobs to the US, impose import tariffs, and scrap or renegotiate existing trade agreements, including the North American Free Trade Agreement (NAFTA). This paper examines county-level presidential voting in the four US states bordering Mexico. Two hypotheses are tested. One, in general, that voters in Mexico-adjacent counties voted differently to voters in non-border-adjacent counties. Two, that voters in border-adjacent counties voted differently based on the degree of interdependence between their county and residents on the Mexican side of the border. The evidence suggests that votes for candidate Trump were negatively related to the degree of county interdependence with Mexico.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".