Race Matters: The Effects of Race in Evaluating Prospective Supervisors
Bibliographic record
Abstract
Building on past research demonstrating racial minority professors continue to face barriers in academia, we examine whether potential graduate students exhibit biases towards prospective supervisors based on race. In line with popular accounts suggesting minority and in particular Asian professors may struggle to recruit graduate students because of stereotypes they are competent but cold, we argue Asian professors will be perceived as less likeable than White professors, which in turn influences students’ intentions to pursue working with professors. Using an experimental design where undergraduate students evaluated a short biography depicting an Asian or White professor as a potential supervisor, we found participants perceived Asian professors as less likeable and indicated slightly lower intentions of pursuing an application with Asian professors. Further, there was a significant and negative indirect effect of professor race on intentions to pursue an application through liking, where students were less likely to indicate pursuing an application with Asian than White professors because of lower liking. Our results suggest potential graduate student’ attitudes toward minority professors may be an underexamined obstacle to those professors’ success and advancement in academia. We discuss contributions to theory and practice.
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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 | 0.021 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".