Marginalization and Leadership: Iranian Immigrant Womenâs Challenges in Canadian Academia and Society
Bibliographic record
Abstract
Iranian women disappointed by increasing social and political instability in their homeland have migrated to Canada to achieve their dream of social rights and justice. However, the tragedy of September 11, 2001 directed considerable hatred toward Middle Eastern people. They did not feel at peace, were not treated with respect, and their presence in the west aroused suspicion. More recently, Iran's president, Mahmood Ahmadinejad is largely seen to be a person who threatens world peace; this negatively impacts Iranians in the west as those remaining in Iran face heavy economic sanctions. In this article, which is a small part of my thesis research findings, I discuss the challenges of first-generation of Iranian immigrant women through their experiences in Canadian graduate schools and workplaces. How do they negotiate the many negative images of Iranians and how do these images shape their experiences within these institutions? First-generation female Iranian graduate students' experiences in Canada are unheard and undocumented. In interviews, these women questioned the ability of neo-liberal capitalist schools to connect them to Canadian society while honouring their Iranian origins and identities and enabling them to achieve their goal to live and study in Canada. I argue that Iranian immigrant women experience a double exclusion both at school and in the workplace despite their willingness to engage with both places. This dual exclusion is an enormous source of pressure on their minds and spirits. The goal of this research is to give these women a voice. Policy makers in school and workplaces will benefit from the findings of this research, which calls for significant changes to realize social justice in Canadian society.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.062 | 0.017 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".