Taking Intersectionality Seriously: Teaching Challenges and Practices in the Classroom
Bibliographic record
Abstract
Intersectionality refers to the combined effects of multiple forms of discrimination. Since the 1970s, critical pedagogy and feminist theories on intersectionality have shed light on the intertwinement of domination systems, namely racism, class, sexism, and ableism. They have brought important tools to think about the mechanisms at play within classrooms and liberatory and transformative ways of learning. However, this conversation comes mostly from the margins and is just starting to enter North American business schools. We need to understand how intersectionality plays out when teaching in a dominant context. In this essay, I present five real classroom situations as they unfolded in Canadian universities, and I explore how an intersectionality lens can help in grasping what is sometimes occurring beneath the surface in the classroom. Then, I propose concrete strategies for addressing challenging situations that are likely to arise in most business school courses. I argue that management educators have a duty not only to recognize their privileges and use them to confront systemic oppression but also to avoid propagating knowledge, theories, or ideas rooted in harmful and unquestioned assumptions that lack sensitivity to intersectionality.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".