From Victimhood to Mobilization: Stories of Muslim Women Activists in London, Ontario Responding to The Afzaal Family Murders
Bibliographic record
Abstract
This thesis explores the complex responses of seven Muslim women activists in London, Ontario, following the tragic Afzaal family murders.Employing a feministcentered methodology, the study utilizes storytelling and qualitative interviews to probe the emotional and social impacts on the Muslim community in the wake of this terrorist attack.The research highlights how these women, engaged in various fields including nonprofit work, campus activities, the arts, and youth initiatives, exhibit resilience and agency.Active listening and empathetic engagement, emphasizing "story-listening," are key methods in this study that align with progressive anti-colonial research practices.The study identifies three critical themes: Reclaiming Voices, where the activists take control of their narratives, assert their rights to tell their stories authentically and on their own terms; Navigating Ongoing Trauma, acknowledging the persistent effects of Islamophobia as a continual reality in their lives; and Collective Care and Community Building, underscoring their involvement in a broader support and solidarity network.These themes illuminate how the women move beyond a narrative of victimhood, positioning themselves as proactive agents in community healing and change.The findings challenge the conventional damage-centric narrative prevalent in research about Muslim women, focusing instead on their dynamic roles in fostering community resilience and activism.This research advocates for a shift in future studies from merely documenting suffering to a broader analysis of social and historical contexts.The activism strategies of these women provide insights into coping and resistance, challenging traditional narratives in feminist and decolonial research.This study significantly contributes to our understanding of the unique challenges faced by Muslim women activists, celebrating their strength, agency, and crucial role in community transformation.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.018 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".