Reclaiming Narratives - Muslim Women Navigating Activism in Educational Research Implications and Recommendations for Educators
Bibliographic record
Abstract
This chapter delves into the intricate dynamics of activism within educational research within the context of resistance and justice within settler-colonial states from Turtle Island and beyond. Drawing inspiration from Eve Tuck's (2010) concept of shifting from damage-centered research to desire-based research and Sara Ahmed's (2010) work on embodying what it means to be a killjoy, we endeavour to confront and address prevailing tensions we face as visibly identified Muslim women researchers and educators. We position ourselves to navigate the complexities of our lived experiences and advocate for justice in the current climate. We come together from Pakistani and Palestinian familial lineages to share our lived experiences and specific testimonies of ‘othering’ in educational research and activism. Using an anti-colonial and desire-based framework, we explore the framing and tensions of Orientalism and the struggle against it. We also contemplate our identities, positionalities and stances within educational research. Drawing strength from Indigenous cultures and Islamic philosophies, we seek to advocate for disruption, refusal and subversion, essential to activist research. We conclude with implications for educators, universities, researchers, schools, communities, and beyond. We aim to illuminate the paths we navigate as activist researchers, harnessing our collective experiences and reframing the research approach through a desire-based approach.
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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 | 0.026 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.027 | 0.043 |
| Scholarly communication | 0.021 | 0.014 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".