A Road Map for an Emerging Psychology of Social Class
Bibliographic record
Abstract
Abstract Though the scientific study of social class is over a century old, theories regarding how social class shapes psychological experience are in their infancy. In this review, we provide a road map for the empirical study of an emerging psychology of social class. Specifically, we outline key measurement issues in the study of social class – including the importance of both objective indicators and subjective perceptions of social class – as well as theoretical insights into the role of the social class context in influencing behavior. We then summarize why a psychology of social class is likely to be a fruitful area of research and propose that social class environments guide psychological experience because they shape fundamental aspects of the self and patterns of relating to others. Finally, we differentiate social class from other rank‐relevant states (e.g., power) and social categories (e.g., race/ethnicity), while also outlining potential avenues of future research.
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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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.031 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 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".