Racialized and Colonial Experiences of Graduate Teaching Assistants: Oppression, Meaning and Transformation
Bibliographic record
Abstract
Despite significant research on racialized inequity in higher education, little research examines the experiences of TAs who are Black, Indigenous or people of colour (BIPOC) in Canada. Based on 37 semi-structured interviews with BIPOC domestic graduate student TAs, this article explores the racialized and colonial oppression of BIPOC TAs: They confront racism from students (disrespect, challenges to their authority and expertise; microaggressions and discrimination; exposure to racist and colonial discourse in students’ work). They also experience challenges with TA supervisors (not being heard; discriminatory discipline; political alienation). Finally, BIPOC TAs face racism within administration (“unthinking” racism; discrimination). Yet, BIPOC TAs also experience TAing as a source of meaning (love of teaching; pride in their pedagogy; desire to help students) and transformation (fostering critical thinking; reflecting students’ identities; being a role model). Although BIPOC TAs are marginalized by institutional whiteness-coloniality, they engage in resistance from the position of marginality. Given the vital role TAs play in today’s universities, attention is needed to address the inequities faced by BIPOC TAs and to support their role in transforming the university.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.019 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".