She just wanted to "be like any Canadian teenager" : representations of muslim women in local newspapers, legislation and political debates in post 9/11 Canada
Bibliographic record
Abstract
This MRP, "She just wanted to "be like any other Canadian teenager": Representations of Muslim Women in Canadian newspapers, legislation, and political debates in the context of post 9/11 Canada," analyzes local newspaper coverage of the murders of Muslim women. It focuses, in particular, on the 2007 murder of Aqsa Parvez in Mississauga, Ontario, but also considers reports on the 2009 murder of Rona Mohammad, Zainab Shafia, Sahar Shafia, and Geeti Shafia in Kingston, Ontario. Alongside this analysis this MRP considers the Zero Tolerance for Barbaric Practices Act, Quebec's Bill 94, and political debates at the federal and provincial level about these pieces of legislation. Post 9/11 discourses in newspapers, legislation, and political debates mobilize gendered and racialized stereotypes about Muslim women and men, which have particular consequences for Muslim women and men living in Canada. They raise important historical questions: What is the history of women like Aqsa Parvez, Rona Mohammad, Zainab Shafia, Sahar Shafia, and Geeti Shafia in Canada? What stories are shared about them, and the men in their families and communities, in these public discourses? How does this impact ideas about the past and present of Muslim people in Canada? To answer these questions, I examine local newspaper coverage from the Mississauga News and the Kingston Whig-Standard, as well as, Canadian legislation and political debates. I argue that these discourses do little to complete the story of Muslim women in Canada, such as Parvez, and even less to challenge incomplete understandings of Muslim people and Muslim culture, which continue to be deployed in media and inform those ignorant about the Muslim community in Canada. I argue that when media, politicians and lawmakers generalize, prescribe, or make assumptions about people they perpetuate, often with dangerous consequences, stereotypes and Islamophobia and do not challenge structural inequalities that lead to violence. This MRP highlights the persistence and growth of such misunderstandings for Muslim people in Canada in the post-9/11 era and contributes to historical discussions about the Canadian nation, which have highlighted the gendered and racialized dimensions of multiculturalism, immigration, and citizenship by extending these discussions to ideas about Muslim men and women.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.038 | 0.019 |
| Scholarly communication | 0.012 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".