Suspicious Minds: Unexpected Election Outcomes, Perceived Electoral Integrity and Satisfaction With Democracy in American Presidential Elections
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A great amount of research has noted the existence of a gap between election winners and losers in relation to perceptions of electoral fairness and satisfaction with democracy. One aspect of the winner-loser gap that has been overlooked is the impact of citizens' expectations about election outcomes on these attitudes. More precisely, how do citizens react to unexpected defeats and victories? Are individuals on the losing side less critical of the electoral process or dissatisfied with democracy when they recognize beforehand that their favourite party or candidate was likely to be defeated? Does experiencing a surprise victory lead to a boost in perceived electoral integrity or democratic satisfaction? To answer these questions, I use data from the 1996, 2000, 2004, 2012, 2016 and 2020 ANES. While there is little evidence that expectations exert a major influence on post-election attitudes, outcome unexpectedness seems to have decreased confidence in the vote counting process among losers, independents and even winners in the 2020 election. The results show the considerable influence that fraud claims and conspiracy theories can have on public opinion when elected officials and candidates push a consistent story line of electoral malfeasance and corruption in an effort to denigrate political opponents.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it