Bibliographic record
Abstract
This paper analyses relationships between whiteness and damage in the university classroom through a focus on two contemporary areas of critical education in Canada: raising white racial consciousness and truth and reconciliation between Indigenous and non-Indigenous people. First, whiteness is damage-producing – it orients anti-racist education towards white students and their needs, there by harming the well-being and constraining the education of non-white students. Second, whiteness gravitates towards what Unangax scholar Eve Tuck calls “damage-centred approaches,” which objectify non-white suffering, pathologising Indigenous peoples whilst obfuscating the ongoing reproduction of racism and colonialism. As such, white educators must remain assiduously vigilant about a key tension regarding whiteness and damage: that our pedagogical focus on racial and colonial oppression can simultaneously raise critical consciousness and divert attention away from more fundamental interrogations of whiteness, agency, and relationality within a systemically racist social order. The article closes with some considerations for educators in terms of addressing complicity in their institutions.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.021 | 0.034 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.019 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".