Too Muslim to Be a Feminist and Too Feminist to Be a Muslim? Locating Lived Experiences of Feminism and Muslimness in Social Work Academe
Bibliographic record
Abstract
In this paper, two authors seize space as Muslim women feminist social work educators and researchers. We challenge and hopefully silence homogenizing, essentialist and Islamophobic constructions. The first author is a hijabi, Indo-Caribbean, able-bodied cis-heterosexual Muslim feminist; the second author is a disabled, queer Muslim of South Asian heritage. We identify as racialized and firmly rooted in intersectional critical feminist perspectives. Using an autoethnographic, conversation-based approach, we share our narratives (lived experiences) in social work academe. Navigating feminisms, Muslimness, strategic essentialism and Islamophobia while engaging in a critical praxis, we attempt to bring together contradictory discourses for critical examination. We engage with the following questions: How do Muslim women fit (or not fit!) in social work academe? How do Muslim women fit (or not fit!) in critical social work feminist spheres? And what do Muslim feminist futures look like in social work academe? Our lived experiences as racialized Muslim feminists are standpoint perspectives which offer situated knowledges that disempower dominant social work discourses. Social work can no longer be reactionary and preserve the status quo; it must move forward with foresight and be an active player in dismantling inequities.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.037 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".