Personality Traits and the Early Origins of Political Sophistication: Openness to Experience or Intellectualism?
Bibliographic record
Abstract
Abstract Recent research using the Big Five model of personality traits has highlighted the importance of personality traits to explaining diverse political behaviours and attitudes. The trait labelled openness to experience has also been found to positively affect political knowledge. This investigation seeks to distinguish two different components of openness: the aesthetic and the intellectual facets. An analysis of the 2015 Canadian Election Study (CES), the 2012 American National Election Study (ANES) and the 2013 ANES Recontact Study was conducted to explore this question. Openness had no significant impact on political knowledge when a measure that more precisely targets intellectualism, as represented by need for cognition, was included. However, open individuals did exhibit higher levels of interest in politics. Finally, openness to experience and need for cognition fostered political knowledge with frequency of political discussion and exposure to disagreement in the CES respondents (Canadians) but not in the ANES respondents (Americans).
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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 | 0.001 | 0.007 |
| 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 | 0.014 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".