Stereotypes and social decisions: The interpersonal consequences of socioeconomic status.
Bibliographic record
Abstract
Perceptions of socioeconomic status (SES) can perpetuate inequality by influencing interpersonal interactions in ways that disadvantage people with low SES. Indeed, lab studies have provided evidence that people can detect others' SES and that they may use this information to apply stereotypes that influence interpersonal decisions. Here, we examine how SES and SES-based stereotypes affect real-world social interactions between people from a socioeconomically diverse population. We used the computer-mediated online round-robin method to facilitate interactions among 297 participants from across the U.S. Participants completed a series of dyadic interactions with other participants in virtual rooms in which they discussed a recent negative consumer experience. After each interaction, they judged the interaction partner's SES, personality traits, and credibility of their consumer experience. Results showed that people perceived SES with moderate accuracy in the interactions, which elicited negative interpersonal stereotypes of low-SES individuals for all 12 of the personality traits measured. People also preferred to affiliate with others with high SES, had more sympathy for them, and found their experiences more credible. SES-based interpersonal stereotypes about personality traits mediated these associations. The perception of SES in real-time interactions thus appears to activate stereotypes that guide social judgments, supporting the hypothesis that interpersonal effects contribute to economic inequality. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".