Locating Black Muslim Life: Critical Race, Intersectional & Transnational Feminist Approaches to Securitization
Bibliographic record
Abstract
This literature review will draw from Black feminist scholars (Crenshaw, 1991; Browne, 2015) to explore how the imagined migrant-as-threat narrative, is a racist discourse which influences Canadian and U.S. policies and legislation such as the Travel Ban, War on Terror and Anti-Terrorism Act (Bill C-51) and constitutes a form of securitization that targets certain migrants. Specifically, securitization is the process of placing certain individuals into an identity-based profile built on racist constructs such as the “terrorist”; these individuals are categorized as criminals engaging in harmful behaviour that is allegedly threatening to society, national security, and the state. However, there are research gaps within the field of securitization. I argue that the securitization process does not encompass the intersections of Islam, gender and Blackness. I propose to integrate a critical race, intersectional and transnational feminist approach to understand how laws and policies such as Bill C-51 targets Black Muslims. Thus, these perspectives can provide a more nuanced approach to understanding the experiences of Black migrants globally undergoing the securitization process which limits their ability to travel, migrate, and navigate spaces.
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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 | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.016 | 0.038 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".