Outcome‐Based Perceptions of Morality and Support for Political Candidates
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
Abstract During the 2016 U.S. presidential election, Trump supporters expressed that they believed Trump was a moral candidate because of his past successes. Such statements are consistent with our argument that people judge others’ morality based on their success and failure‐related outcomes, even though these outcomes are usually associated with judgments of competence. Moreover, we argue that these outcome‐based perceptions of morality play a crucial role in responses to political candidates, independent of perceived competence. In three experiments, we manipulated a hypothetical candidate's outcomes (past successes vs. failures, or success in light of past misfortune vs. good fortune). We examined the effect of the manipulation on perceptions of the candidate's morality and competence, as well as support for the candidate (e.g., voting intentions). Across the three experiments, candidates’ outcomes affected not only perceptions of their competence, but also their morality. In turn, outcome‐based perceptions of competence and morality independently predicted candidate support. Our findings have implications for how people responded to the campaigns in the 2016 U.S. presidential election, especially the campaign run by Donald Trump.
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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 | 0.000 | 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 | 0.000 | 0.001 |
| 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 it