Teaching while Black: racial dynamics, evaluations, and the role of White females in the Canadian academy in carrying the racism torch
Bibliographic record
Abstract
For racialized academics, life in the academy can be marred by racial violence that leaves them caught between their commitment to their craft, desire for educational attainment and development, and the mental anguish that can dominate their existence. Drawing from experiences of the author and other Black faculty members in Canadian tertiary academic institutions, I provide a theoretical exposition recognizing the role of narratives as an act of counter-storytelling. I draw upon Black feminist epistemology, critical race theory, and critical theory to examine how the experiences of Black academics remain under-theorized, marginalized, and often erased within ‘strong/angry Black woman/man’ caricatures. I highlight how racial evaluation filters reinforce racism and affects the careers of Black academics. I also discuss the role that White women, who are charged with decision-making power, have come to play in carrying the ‘racism torch’ in the academy while adhering to the tropes of innocence.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.062 | 0.036 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".