Gendered Islamophobia in the Case of the Returning ISIS Women: A Canadian Narrative
Bibliographic record
Abstract
In February 2019, the case of Shamima Begum, hit the headlines. Begum, one of the three East London girls who had left the UK for Syria in 2015, was located in a refugee camp in Syria. Tagged as an “ISIS bride,” Begum's case raised the profile of Muslim women who had voluntarily left their home countries to join the Islamic State and were now seeking to return. In this paper, I focus on the Canadian women returnees who were and, in some cases, remain wives of ISIS soldiers. I pay particular attention to how they are framed in the Canadian media and the audience response to their portrayals. Against a backdrop of the media's representation of these women, I examine the comments that audience members posted after a three-part series on the returning ISIS members was broadcast on the Global Television Network during the month of October 2018. Global TV is a 24/7 news channel that can be streamed online on various platforms. I contend that the construction of the returning wives and the responses the series elicited are reflective of the larger currents of racism and Islamophobia that circulate within Canadian society and that have become amplified since the inception of the War on Terror. However, they take on a distinct hue with respect to the framing of gendered agency and critically heighten the affective charge around the issue of returning ISIS fighters and the women who joined the movement. In this sense, the technology making online commenting possible has escalated the extent and intensity of Islamophobia. This article also seeks to demonstrate how Islamophobia is yoked to and animates an anti-government discourse. Thus, in contrast to Canada's projected national image as a benign, multicultural nation, the user-generated comments paint a picture of a white nation that is overrun with and taken advantage of by racialized minorities.
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.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.062 | 0.026 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".