On the back of blackness: contemporary Canadian blackface and the consumptive production of post-racialist, white Canadian subjects
Bibliographic record
Abstract
This article is based on a qualitative research project examining the phenomenon of contemporary Canadian blackface. It addresses the discursive juxtaposition of blackface with the claim to Canadian racial progressiveness that typically attends public debates about blackface. I argue that blackface and the discourses defending it are forms of racial consumption through which ostensibly progressive white subjectivities are secured. I further argue that contemporary Canadian blackface discourse is postracialist in its ability to juxtapose racist expression with claims of racial transcendence, and I identify this postracialism as a long-standing feature of Canadian national narratives that are partially constructed through revisionist understandings of the nation’s relationship to blackness, and against an ostensibly more virile racism in the US. This analysis reminds us of the symbiotic relationship between racial fetishization/fascination as found in contemporary blackface, the foundational white supremacy of the Canadian settler-colonial context, and the always uneven terms upon which blackness is included in Canada. It clarifies what is at stake for Canadians who participate in blackface and in defending it, and helps us to understand the pedagogical import of both blackface and Canadian egalitarianism for perpetuating anti-blackness in Canada.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.047 | 0.028 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".