De-Islamizing Sikhaphobia: Deconstructing structural racism in Wisconsin gurdwara shooting 10/12
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it