Introduction to Special Issue: Whiteness in the Age of White Rage
Bibliographic record
Abstract
This Special Issue—“Whiteness in the Age of White Rage”—names and interrogates what is implicit in anti-racist, Indigenous, and whiteness studies: white rage. Drawing on Carol Anderson’s White Rage: The Unspoken Truth of Our Racial Divide (2017), we invited scholars to explore empirical and theoretical inquiry of how rage is a defining characteristic of settler colonialism, whiteness, and white supremacy in Canada. In this Introduction we elaborate how contemporaneously, historically, and theoretically a vital dimension of the configuration of whiteness in Canada is the normalization of rage as a property right of whiteness. Presently, as fascism is once again a global phenomenon, there is an opportunity for critical scholarship on whiteness in Canada to name and explicate the social effects and quotidian mobilization of rage in conservative and liberal articulations of white supremacy. We offer a general outline to the theme of whiteness in the age of white rage to introduce nascent scholarship that builds on the scholarship of Black, Indigenous, people of colour, and critical whiteness scholars.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.062 | 0.016 |
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".