Erasing Race in the Canadian Media: The Case of Suaad Hagi Mohamud
Bibliographic record
Abstract
This article proposes a critical race theoretical approach to news discourse to counter the erasure of race in Canadian public discourse, using media coverage of the Suaad Hagi Mohamud affair as a case study. Between May and August 2009, Mohamud, a Canadian of Somali origin, was stranded in Nairobi, Kenya, because Canadian authorities voided her passport on the erroneous grounds that she was an impostor and consequently procured her prosecution by Kenyan authorities. While Mohamud’s case received extensive media coverage in Canada, much of the coverage failed to interrogate the possibility that her experience was racially motivated, despite facts that should have raised such concerns. Consequently, this article adopts a critical race perspective in discussing mainstream media coverage of the case and suggests alternative media discourses that engage with the race question in relevant cases.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.087 | 0.020 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".