Cognitive Reflection and the 2016 U.S. Presidential Election
Bibliographic record
Abstract
We present a large exploratory study ( N = 15,001) investigating the relationship between cognitive reflection and political affiliation, ideology, and voting in the 2016 Presidential Election. We find that Trump voters are less reflective than Clinton voters or third-party voters. However, much (although not all) of this difference was driven by Democrats who chose Trump. Among Republicans, conversely, Clinton and Trump voters were similar, whereas third-party voters were more reflective. Furthermore, although Democrats/liberals were somewhat more reflective than Republicans/conservatives overall, political moderates and nonvoters were least reflective, whereas libertarians were most reflective. Thus, beyond the previously theorized correlation between analytic thinking and liberalism, these data suggest three additional consequences of reflectiveness (or lack thereof) for political cognition: (a) facilitating political apathy versus engagement, (b) supporting the adoption of orthodoxy versus heterodoxy, and (c) drawing individuals toward candidates who share their cognitive style and toward policy proposals that are intuitively compelling.
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.
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 | 0.001 | 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 | 0.002 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".