Underemployed and Penalized: Education–Occupation Mismatch and Racial/Ethnic Inequality among Highly Educated Workers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
How does education–occupation mismatch shape racial/ethnic labor market inequality among highly educated workers? Bridging the literatures on racial/ethnic discrimination and labor market signaling, we propose a new concept, “racialized signaling,” to explain inequality in the college-to-work transition, operationalized through education–occupation mismatch. We then use longitudinal data to examine the labor market consequences of racialized signaling, analyzing vertical and horizontal dimensions of mismatch. We find that Black and Hispanic graduates experience the negative consequences of mismatch most strongly at the point of occupational allocation relative to their White peers, whereas Asian graduates experience the greatest negative consequences of mismatch regarding wage penalties. Advanced degrees, STEM degrees, and degrees from more selective institutions have some moderating effects, but they do not fully level the playing field for minority graduates. Overall, our findings suggest education–occupation mismatch is a powerful, although heterogeneous, mechanism reproducing racial/ethnic inequality among the most educated segment of the U.S. population.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it