Eritrean Women in Canada Negotiating
Bibliographic record
Abstract
encounter all the risks and dangers that men face in flight, resettlement, and exile as well as additional threats of sexual assault and exploitation. Pour faire contre poidr au manque ditnalyses sur le genre dans les Ptudes sur les migrationsforcPes et ir la tendance h uoir I'expPrience des emmes comme des ajouts, cet article examine li'n-teraction complexe entre lesgenres, les classes, les ethnies et les gknirations, uPcue comme des expiriences quoti-diennes dans une cornmunautP peu connue des immigrantes et rejiugihes de I'EtythrPe. Little attention has been given to the experience of Eritrean immigrant and refugee women in Canada. This is part of a more general lack of gender analysis in studies on forced migra-tion and a tendency to see women's issues as adds-on, thus marginalizing them (Indra 1989, 1999). A growing number of studies are bringing wom-en's issues to the centre; however, engendering knowledge about immi-grants and refugees is not a matter of studying women alone but of exam-ining immigrant and refugee issues in terms of gendered social relations. Utilizing feminist theory, whichviews gender as a relational concept rather than simply equating gender with women, this article examines thecom-plex interaction of gender, class, race, and generation as lived in the every-day experiences ofEritrean women in Canada. By addressing everyday ex-periences, we can observe strengths and see how past and current policies affect this diaspora population in Canada. Our discussions are based on work conducted in five Canadian cities 98 women participated in interviews from 1990 to 1996. Most were 25 to 45 years old; 32 women had never been married, the rest of the participants either were or had been married.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.035 | 0.007 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".