Social-Class Disparities in Higher Education and Professional Workplaces: The Role of Cultural Mismatch
Bibliographic record
Abstract
Differences in structural resources and individual skills contribute to social-class disparities in both U.S. gateway institutions of higher education and professional workplaces. People from working-class contexts also experience cultural barriers that maintain these disparities. In this article, we focus on one critical cultural barrier—the cultural mismatch between (a) the independent cultural norms prevalent in middle-class contexts and U.S. institutions and (b) the interdependent norms common in working-class contexts. In particular, we explain how cultural mismatch can fuel social-class disparities in higher education and professional workplaces. First, we explain how different social-class contexts tend to reflect and foster different cultural models of self. Second, we outline how higher education and professional workplaces often prioritize independence as the cultural ideal. Finally, we describe two key sites of cultural mismatch—norms for understanding the self and interacting with others—and explain their consequences for working-class people’s access to and performance in gateway institutions.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".