Ideology trumps self-interest: continued support for a political leader despite disappointing tax returns
Bibliographic record
Abstract
People presumably strive to maximize their own benefit whenever possible, so it is puzzling when they vote for leaders who may not have their best interest at heart. We tested whether support for a political leader is diminished when supporters learn they are financially disadvantaged by the leader’s policies. In a two-stage experiment (Time 1 n = 601, Time 2 n = 343) with pre-registered hypotheses, Trump voters predicted their expected tax refund (or payment), and then reported their tax outcome immediately after the filing deadline. Afterwards, we confronted half of the participants with the discrepancy between their actual and predicted tax outcome. Having lower-than-expected tax outcomes was not associated with reduced support for Trump either on its own, or in combination with being reminded of this outcome. However, it led participants who were dissatisfied with their tax outcome to downgrade the importance of lowering taxes, possibly in an effort to reduce dissonance and justify continued support for Trump. Subjective tax outcome satisfaction did predict Trump support, but was dwarfed in magnitude by other variables such as system justification and political orientation. Thus, people may find ways to rationalize information that goes against their self-interest into their partisan world-view.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".