Queer inclusion precludes (Black) queer disruption: media analysis of the Black lives matter Toronto sit-in during Toronto Pride 2016
Bibliographic record
Abstract
During the 2016 Toronto Pride Parade, an annual leisure event which attracts thousands of spectators, the group designated by Pride Toronto as the ‘Honoured Group,’ Black Lives Matter Toronto (BLM-TO), held a sit-in to raise attention to what they termed anti-Black racism in both Pride Toronto and the Toronto Police Service. I employ a qualitative content analysis to examine reports in queer and mainstream media. I identify three thematic representations apparent in many reports: a narrative of terrorism, a discourse framing BLM-TO as an aggressor, and language identifying BLM-TO as an outsider to the queer community. The discussion section seeks to examine how media reports removed the action from the context of racism in which BLM-TO asserts the action occurred. I also examine how, through a disavowal of BLM-TO and the sit-in, a sense of Canadian and queer community was reasserted. I also suggest that BLM-TO’s sit-in disrupted the legitimacy of the discourse of Canadian multiculturalism. Finally, I argue that the paucity of reports on the action in queer media outlets suggests that issues of racism come to be relegated to the periphery through a larger process of centring Whiteness within the identity category of ‘queer.’
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".