"I've said it before and I will say it again": Repeating statements made by Donald Trump increases perceived truthfulness for individuals across the political spectrum
Bibliographic record
Abstract
Fact-checking organizations have reported that Donald Trump is prone to repeating patently false statements. Previous research has shown that repetition increases perceived truthfulness of even implausible statements (also known as the illusory truth effect). However, other research has shown that people may engage in motivated reasoning when interacting with someone from a different ideological group. We measured the effect of repeating statements made by Donald Trump on perceived truthfulness. Participants (N = 465) rated the truthfulness of different statements made by Donald Trump, some of which were repeated from an earlier phase of the experiment. Our results are striking. We observe an overall effect of repetition on perceived truthfulness that is equally robust regardless of political affiliation. Our results suggest that ideologically-motivated beliefs do not modulate the effect of repetition on perceived truthfulness of statements.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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; 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".