On Black Canadian Media Studies: A Conversation with Cheryl Thompson
Bibliographic record
Abstract
On February 17, 2022, the Canadian Journal of Communication met with Cheryl Thompson (2021) via Zoom to discuss her latest book, Uncle: Race, Nostalgia, and the Politics of Loyalty, and the possibilities of a Black Canadian media studies. Dr. Thompson is a professor in the School of Performance in X University,1Toronto, Canada, and author of Beauty in a Box: Detangling the Roots of Canada’s Black Beauty Culture. She has published articles in Emergent Feminisms: Challenging a Post-Feminist Media Culture, the Journal of Canadian Studies, Canadian Journal of History Annales canadiennes d’histoire (CJH / ACH), and Feminist Media Studies. She has also published in The Conversation, Toronto Star, Montreal Gazette, Spacing, Herizons, Halifax Coast, and Rabble.ca. Thompson grew up in Scarborough and currently resides in Toronto. She is working on a book that addresses Canada’s history of blackface.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.096 | 0.023 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".