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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".