“We Say Nah!”: Refusals and Collaborative Autoethnographic Storytelling by and for Black Womxn in Canadian Academia
Bibliographic record
Abstract
The intersectional challenges faced by Black womxn graduate students in Canadian post-secondary education and academia are grounded in misogynoir (gendered racism) and microaggressions. In this collaborative critical autoethnography, the authors unveil the legacy of Black feminist theory in academia’s pursuit of equity, diversity, and inclusion. This work centres on a profound act of refusal; Black womxn scholars refuse prescribed narratives and imagine other possibilities of being and knowledge creation. By practicing refusal through the act of scholarly storytelling, the authors collaborate in reflexive dialogue and writing to reweave the narrative tapestry for Black womxn. The threefold purpose of the article is to support Black womxn graduate students and faculty in redefining their own narratives; foster collective healing, resilience, and strength among the authors; and challenge dominant perspectives on Black scholars/scholarship. Through the lens of Black livingness, the authors assert that refusal is a powerful tool for dismantling systemic inequalities within academic spaces.
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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 | 0.023 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.063 | 0.052 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.005 | 0.009 |
| 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".