Attitudes Toward Presidential Candidates in the 2012 and 2016 American Elections: Cognitive Ability and Support for Trump
Bibliographic record
Abstract
Using data from the American National Election Studies, we investigated the relationship between cognitive ability and attitudes toward and actual voting for presidential candidates in the 2012 and 2016 U.S. presidential elections (i.e., Romney, Obama, Trump, and Clinton). Isolating this relationship from competing relationships, results showed that verbal ability was a significant negative predictor of support and voting for Trump (but not Romney) and a positive predictor of support and voting for Obama and Clinton. By comparing within and across the election years, our analyses revealed the nature of support for Trump, including that support for Trump was better predicted by lower verbal ability than education or income. In general, these results suggest that the 2016 U.S. presidential election had less to do with party affiliation, income, or education and more to do with basic cognitive ability.
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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 | 0.000 |
| Science and technology studies | 0.002 | 0.015 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".