North? Exploring Whiteness, Privilege, and Identity in Education
Bibliographic record
Abstract
If White people do not know they are White, how can those who are in positions of power, many of whom are White, effectively understand and challenge racism and unearned privilege? (Carr and Lund, p. 2) This recent edited book takes as its goal to explore “what does Whiteness1 look like in general and in Canada in particular? ” (p. 3) and brings together a variety of authors who explore the individual privileging and institutional pervasiveness of Whiteness from a variety of viewpoints: for example, as a First Nations woman academic; as a White gay man; as a White provincial education policy maker; as a teacher in a northern community, and so on. As one of the chapter authors, Kathleen Berry, observes, while we are familiar with studying the oppression of particular groups in Canadian (and other) societies, what is rare is an acknowledgement of how that oppression links to White privilege. Rather, as George Sefa Dey in the Foreword to the book argues, Whites are more likely to deny race and difference politics as endemic in Canadian society: he observes, for example, “many of the people most imbued with [racism’s] orchestration and manifestation, namely, White people maintain the power and privilege to ignore and dissociate themselves from the experiences of others who are more directly affected or marginalized by racism” (p. vii). This book should serve to alert researchers and teachers to undeniable examples of how racism has been experienced in a wide range of situations (from the perspectives of the colonized, but also from the perspectives of critically aware
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.034 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".