Research note: Examining false beliefs about voter fraud in the wake of the 2020 Presidential Election
Bibliographic record
Abstract
The 2020 U.S. Presidential Election saw an unprecedented number of false claims alleging election fraud and arguing that Donald Trump was the actual winner of the election. Here we report a sur-vey exploring belief in these false claims that was conducted three days after Biden was declared the winner. We find that a majority of Trump voters in our sample – particularly those who were more politically knowledgeable and more closely following election news – falsely believed that election fraud was widespread and that Trump won the election. Thus, false beliefs about the elec-tion are not merely a fringe phenomenon. We also find that Trump conceding or losing his legal challenges would likely lead a majority of Trump voters to accept Biden’s victory as legitimate, alt-hough 40% said they would continue to view Biden as illegitimate regardless. Finally, we found that levels of partisan spite and endorsement of violence were equivalent between Trump and Biden voters.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".