Racialized experiences as in-betweenness in academia
Bibliographic record
Abstract
This article draws on an in-depth narrative of a Chinese woman, early career researcher based in a UK business school, to consider questions of subtle racism in academia. Specifically, engaging with our informant’s testimony, and reading it in the context of critical organizational debates on race, we offer episodic accounts of the subtle racism that she has encountered in academia to conceptualize experiences of in-betweenness of racial minorities excluded from dominant diversity discourses. In her case, subtle racism appears to emanate from a set of gendered and racialized tropes, culminating in the “model minority” myth. This article captures how racism is encountered differently by different populations; specifically, it illuminates how racism materializes in culturally-dependent, idiosyncratic forms, which should not be de-contextualized from the historical, political, and social dynamics that engender it. In so doing, it contributes to recent efforts to speak out against racism in the academy.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.040 | 0.036 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".