Stretched Too Thin? The Paradox of Promoting Diversity in Higher Education
Bibliographic record
Abstract
We examine an important unintended consequence of diversity promotion in higher education. Specifically we demonstrate that departmental efforts to increase the representation of racial minorities, coupled with the limited supply of minority doctoral candidates, leads to an increased prevalence of joint appointments among minority faculty. This outcome is important as joint appointments overexpose faculty members to a set of unique risks that can negatively affect their career advancement. Using comprehensive administrative data from a large U.S. public university from 1990 to 2009, we find that African American assistant professors were four times more likely to be jointly appointed as compared to their white colleagues. We further find that the hiring and joint appointment of African American assistant professors is motivated in part by efforts to increase diversity within departments. Finally, we demonstrate that independent of race, being jointly appointed at the assistant professor level is associated with poorer career outcomes. Ceteris paribus, assistant professors that are jointly appointed in two or more academic departments receive smaller year-over-year raises as a percentage of their income and face lower likelihoods of promotion to tenure. Together, our results highlight the unintended costs of diversity promotion in academia whereby using joint appointments to achieve diversity goals at the level of the academic department can negatively affect the employment outcomes of minorities and work counter to diversity goals at the level of the university.
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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 | 0.006 | 0.021 |
| 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 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".