The transformative and informative nature of elections: Representation, schism, and exit
Bibliographic record
Abstract
In democratic elections, constituents may view unconventional or non-prototypical candidates as attempting to reshape their national identity in the wrong direction. When a non-prototypical candidate actually steps into a leadership role, the group's consensual view of their prototype may shift to position this new leader as prototypical. This process should be bound in member consensus, evidenced by the leader's successful election. The current work examines American Republicans (N = 297) and Democrats (N = 322) before and after the 2016 US election. We focus on Republicans' interpretations of their candidate Donald Trump's prototypicality and ability to bolster or subvert their party identity pre-election. Post-election, we examine changes to these processes, related in part to Republicans' homogenized view of Trump's prototypicality. In comparison, we examine these processes in the Democratic Party. Results suggest that whereas Democrats increased in their desire to leave their party, Republicans decreased in their desire to leave their party, an effect that is related to increasing perceptions of Trump's prototypicality and representation of the Republican Party. These findings have important implications for how the contexts of elections rapidly shape party identity through the election of leaders such as Trump.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| 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; 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".