Disrupting Whiteness Within Academia: Examining the Experiences and Understanding of Whiteness Among White Students Attending Ryerson University
Bibliographic record
Abstract
This narrative qualitative research study explored the experiences and understanding of whiteness from three full-time white students at Ryerson University (RU). The theoretical framework draws from Critical Whiteness Studies (CWS) and Critical Whiteness Pedagogy (CWP). Based on existing literature on whiteness, this study utilized semi-structured telephone interviews with the three participants. The participants were randomly selected through recruitment posting and flyers on social media outlets such as Facebook. Data analysis included a thematic and structure of the narratives of the participants. The findings provided insight into how these white students at RU define whiteness and how they understand whiteness demonstrated in academia and, lastly, whether they have perpetrated or fought against whiteness within their academic institution. The results indicate that whiteness is not easily defined, and academia is incorporating diverse perspectives. This paper concludes with implications and discussion on future social work, followed by the conclusion. Key words: Academia, Critical Whiteness Studies, Critical Whiteness Pedagogy, colonialization, gender, whiteness, white students,
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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.019 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| 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".