A Community Lifeline: Arab-Canadian Feminist Anthologies as a Source of Community Building & Knowledge Production
Bibliographic record
Abstract
This thesis explores how two Arab-Canadian feminist anthologies, Food for Our Grandmothers: Writings by Arab-American and Arab-Canadian Feminists and Min Fami: Arab Feminist Reflections on Identity, Space & Resistance, exemplify resistance literature by fostering community, producing knowledge, and critiquing power dynamics within multiculturalism, assimilation, and through their content and themes. Using a feminist lens, the study examines how these anthologies, published twenty years apart, form a continuum in fostering alliances and resisting discrimination based on identity, race, class, and sexuality. By focusing on these works, the research addresses the gap in scholarly attention to Arab-Canadian literature, positioning them as platforms for discussing their feminism, communicating anti-discrimination discourses, and challenging stereotypical portrayals of Arabs.The methodology includes tracing Arab-Canadian literature's historical landscape, highlighting how these anthologies serve as counter-narratives to dominant multicultural and national discourses. Moreover, the complexities in defining Arab feminists in anthologies help explore cultural identity and their transnational solidarity with other women of colour. Finally, a comparative analysis of the anthologies’ structure, contributors, and thematic content, considering their goals and stated purposes, shows their stylistic innovation and resistance to the canon, examining how these anthologies transcend linguistic, geographical, and cultural boundaries. This research deepens the understanding of Arab-Canadian literature, Arab feminism, and the diasporic relationship to Canada, highlighting how these anthologies challenge racism, neocolonialism, and Orientalism
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.041 | 0.013 |
| Scholarly communication | 0.015 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".