It’s security, stupid! Voters’ perceptions of immigrants as a security risk predicted support for Donald Trump in the 2016 US presidential election
Bibliographic record
Abstract
Abstract We analyzed two datasets to determine the predictive validity of four explanations of support for Donald Trump during the 2016 US presidential election: (a) security concerns regarding immigrants, (b) economic concerns regarding immigrants, (c) cultural concerns regarding immigrants, and (d) social dominance orientation. Results of a two‐phase study ( N = 354) suggested that perceiving immigrants as a security concern was predictive of increased support for and greater odds of voting for Donald Trump three weeks later. Perceiving immigrants as an economic threat predicted odds of voting for Donald Trump, but only among liberals and there was no evidence of cultural concern or social dominance orientation (SDO) predicting support for Donald Trump or odds of voting for Trump. A follow‐up analysis of the cross‐sectional ANES survey corroborated that security concerns were an important correlate of voting for Trump, but also that SDO was correlated with having previously voted for Donald Trump. While our two‐phase study has the benefit of prediction, the cross‐sectional ANES data does not—“predictors” in these data were collected up to two months post‐election.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".