Bibliographic record
Abstract
Reflections on Antiblackness, Nation, and Education through Contemporary Blackface in CanadaBlackface -instances in which non-Black persons temporarily darken their skin with make-up to impersonate Black people, usually for fun, and frequently in educational contexts -constitutes a postracialist pedagogy that propagates antiblack logics.In Performing Postracialism, Philip S.S.Howard examines instances of contemporary blackface in Canada and argues that it is more than a simple matter of racial (mis)representation.The book looks at the ostensible humour and dominant conversations around blackface, arguing that they are manifestations of the particular formations of antiblackness in the Canadian nation state and its educational institutions.It posits that the occurrence of blackface in universities is not incidental, and outlines how educational institutions' responses to blackface in Canada rely upon a motivation to protect whiteness.Performing Postracialism draws from focus groups and individual interviews conducted with university students, faculty, administrators, and Black student associations, along with online articles about blackface, to provide the basis for a nuanced examination of the ways that blackface is experienced by Black persons.The book investigates the work done by Black students, faculty, and staff at universities to challenge blackface and the broader campus climate of antiblackness that generates it.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.017 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".