Stereotypes and Stereotyping: Measuring the Accuracy of Lifestyle-Based Judgments on Political Affiliation
Bibliographic record
Abstract
Abstract People often draw inferences about others’ underlying characteristics from single and static samples of their appearance, such as facial features, or attractiveness. Evidence also suggests that these judgments occur spontaneously and rapidly. Are humans also able to detect political preferences based on appearance? This article examines to what extent observable lifestyle characteristics influence people’s judgments about one’s political affiliation and, more importantly, to what degree these judgments are accurate. A conjoint analysis allows for the identification of the specific lifestyle cues that people use to infer one’s political affiliation. These results are contrasted with a large and unique dataset (n = 64,745), enabling the assessment of how accurate these cues are. Results suggest that certain lifestyle characteristics, such as type of car or leisure activities, are clearly associated with different political parties, at least in people’s minds. Results also suggest that, despite the potential detrimental effects of appearance-based judgments, people are generally pretty good at guessing others’ political preferences. This study contributes to a growing body of research on the relationship between lifestyle and political preferences. More generally, it sheds light on the diagnostic value of appearances in everyday social judgments.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".