Politicians pose left but the voter is always right
Bibliographic record
Abstract
Asymmetry in the perception and expression of emotions in the brain can be observed in the cheek presented in a portrait. The left cheek is more often put forward in an emotional photo, but the context of the portrait is important as leftward posing is attenuated in more serious settings. Similarly, leftward posed portraits are perceived as more emotionally expressive. However, there has not been an investigation into how the perception of left and right poses impacts vote choice. We predicted that, when tasked with identifying the portrait they would vote for and the portrait that appeared more friendly, participants would vote for individuals presenting the right cheek and find those individuals showing the left cheek more friendly. Participants' scores indicated more left cheek poses were selected for friendliness and more right cheek poses for voting, with a significant difference found between the conditions. We predicted that more right cheek poses would emerge when a sample of elected officials' portraits were examined. To the contrary, we found a disconnect with participant vote choice as politicians more frequently presented their left cheek, suggesting that it might be time for politicians to put their right cheek forward.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".