"I guess I'm not alone in this": Exploring racialized students' experiences and perspectives of safer classrooms at McMaster University
Bibliographic record
Abstract
This chapter details the findings of our critical race theory-informed study, which explored how pedagogy can adapt to create, foster, prioritize, and sustain safety for racialized students in the classroom. We invited racialized students to participate in a focus group which researchers designed to be a safe space, unpack their experiences of tokenization, harm, and exclusion in the classroom. Participants described the (a) systemic issues within white-streamed pedagogy, (b) the significance of uncompensated and unrecognized labour in the classroom, (c) classroom experiences of harm, and (d) long-term emotional and academic impacts of racial trauma. Drawing from their recommendations, we emphasize the production of counter-stories that centre the need for safer and more inclusive classrooms within post-secondary institutions. Recommendations offered from participants include ways the administration can materially invest in the safety and well-being of students of colour; implications oriented to instructors, staff, and white- identified students in making classroom spaces more equitable; and reflexive-learning and educational opportunities to change language, curriculum, discourse, and interactions across the institution.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.030 | 0.017 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".