(Un)veiling equity: a critical analysis of the insider and outsider roles of a Muslim female Shi’i educator in academy
Bibliographic record
Abstract
This is a critical personal narrative of a Canadian Middle-Eastern Muslim female Shi’i educator’s experiences in a Western academy. The different cultural and religious backgrounds that shape a Muslim woman’s academic work and her understanding of social justice are described. Specifically, the author describes religio-historical figures and the counter-narratives of women of color in the author’s life responsible for shaping her appreciation of the importance of engaging in social justice and equity. The subjectivity in this work is manifested as an alignment with her religious beliefs and doctrine that draws from a counter-narrative (i.e. the Shi’i narrative of Karbala and, more broadly, the marginalized and dissenting ideological position of Shi’ is in relation to global Islam). Her upbringing with and introduction to Shi’i rituals, empower her understanding towards issues related to justice, equal rights, and loyalty. Throughout her narrative, the author utilizes the role of a researcher as an insider and outsider based on religious and feminist approaches. Three significant themes are noted: (a) the critical role of spirituality as a powerful catalyst and feminist for transformative change; (b) the role of reflexive skills and self-criticism as a means to balance religious and academic identities; and (c) the import role of ‘border-crossers’ in negotiating and connecting between religious and academic worlds. Insights about the experiences associated with minority Shi’i Muslim female graduate educators and the importance of being critically reflective when working towards social justice and equity are provided.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.033 | 0.044 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| 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".