De-Islamizing Sikhaphobia: Deconstructing structural racism in Wisconsin gurdwara shooting 10/12
Bibliographic record
Abstract
On Sunday, 5 August 2012, at approximately 10:00 a.m., an armed Wade Michael Page walked into the Oak Creek, Wisconsin Sikh gurdwara, a place of worship. Page killed six worshippers and injured four others. Although the murderer had links to several white supremacist organizations, authorities would not speculate on the motive of this incident. In fact, the word race was rarely mentioned in relation to this case. The lack of a sustained examination of racism as a motivating factor in this crime was very troubling within the media’s portrayal of this incident. Through a critical analysis of structural racism, this article highlights how the silences of racism, racialized identities, and the connections of racist acts such as the Wisconsin gurdwara murders to hate crimes perpetuates racialized and colonized violence on brown bodies. This structural racism absolves many Americans (and we would add many Canadians) of their deeply rooted racist beliefs and ideologies. By providing a counter-hegemonic narrative, this article discusses how the homogenization of brown bodies, such as Sikhs and Muslims, has very real material consequences in a North American context. Finally, this article discusses the problematic framing of this incident as ‘Domestic Terrorism’ and the importance of de-Islamizing Sikhaphobia in the post-9-11-2001 context.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.015 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".