Jagmeet’s Kairotic Challenge: Darkface, Turbans, and Hypocrisy Upwards
Bibliographic record
Abstract
Things did not look good for Jagmeet Singh going into his first federal election as the only non-White leader (ever) of a federal party in Canada, a country with a long and ongoing history of racist hypocrisy. The media had not helped, having framed Singh in exoticizing terms, associating him with terrorism and relentlessly metonymizing his “electability” by the turban he wears. But that election featured disturbing revelations of Prime Minister Justin Trudeau’s pre-political penchant for dressing up in racialized costuming. Photographs and videos of the famously progressive prime minister in brown-and blackface shook the election and shook the country. They also provided an opportunity-charged moment, a kairotic opening, for Singh to model an ethos of compassion and prudential thoughtfulness, and for the country to face its hypocrisy. This essay focuses on addressivity and deixis in Singh’s official statement and its foreshadowing tweet for the way they bend that hypocrisy, in the terms of Wayne C. Booth, upwards. The lessons Singh’s response provides for those of us steeped in Whiteness and performing progressive politics are simple: recognize what you have done, recognize what you are doing, and keep aspiring to the fully realized and inclusive compassion you already believe you have.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.032 | 0.016 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 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".