Changing the Landscape: A critical race informed narrative inquiry of a Canadian university told by Black, Indigenous, and People of Colour (BIPOC) students
Bibliographic record
Abstract
The experiences of Black, Indigenous, and People of Colour (BIPOC) students in higher education are considerably different from the experiences of non-BIPOC students. We concluded that structural racism is deeply embedded in the programs, policies, and practices in higher education. Although universities create anti-racism initiatives, the findings of the study indicate that BIPOC students are neither consulted nor included in the development of resources that are aimed to benefit them. The purpose of the study is to build knowledge with Canadian BIPOC students to develop learning resources for the university leadership. A needs assessment was conducted with BIPOC students at a mid-sized Canadian university, Queen’s University in Kingston, Ontario. The study is a narrative inquiry qualitative study design with a critical race theory lens using community based participatory research principles. Based on 13 counter-narrative interviews, the research explores the results of the needs assessment in terms of policy change from the leadership perspective. The research team consisted of a steering committee of five BIPOC university students and the thesis advisory committee. We engaged with BIPOC students throughout the research process, from the refinement of the research questions to the creation and dissemination of two knowledge translation products. We established recommendations for the leadership that highlight the challenges BIPOC students experience, as well as strategies to address the challenges. Additionally, we co-created a learning resource for the university faculty and staff about what BIPOC students need to support their participation in student life. Working with BIPOC students is a powerful method to dismantle structural racism in programs, policies, and practices in higher education.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.062 | 0.037 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.006 | 0.010 |
| 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".