Political taste: Exploring how perception of bitter substances may reveal risk tolerance and political preferences
Bibliographic record
Abstract
Risk is endemic to the political arena and influences citizen engagement. We explore this connection by suggesting that risk-taking may be biologically instantiated in sensory systems. With specific attention to gender and gender identity, we investigate the connections between self-reported bitter taste reception, risk tolerance, and both of their associations with political participation. In three U.S. samples collected in 2019 and 2020, participants were asked to rate their preferences from lists of foods as well as whether they detected the taste of the substance N-Propylthiouracil (PROP) and, if so, the strength of the taste. In this registered report, we find that self-reported bitter taste preference, but not PROP detection, is positively associated with higher levels of risk tolerance as well as political participation. The pattern with gender and gender identity is mixed across our samples, but interestingly, we find that sex-atypical gender identity positively predicts political participation.
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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 | 0.001 | 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 | 0.002 |
| 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 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".