Marlene Epp, Franca Iacovetta, and Frances Swyripa, editors. Sisters or Strangers?: Immigrant, Ethnic, and Racialized Women in Canadian History.\n
Bibliographic record
Abstract
Sisters or Strangers?: Immigrant, Ethnic, and Racialized Women in Canadian History aims to expand existing representations and analytical frameworks of women of colour in immigrant and women's history.Identifying the 1986 collection Looking into My Sister's Eyes: An Exploration in Women's History as a ground-breaking, yet lacking, study of female immigrants and minority women in Canada, the editors and the seventeen contributors of Sisters or Strangers?challenge the earlier text's premise of women of colour in Canada as sisterly members of closely knitted, and rather homogeneous communities.Instead, the contributors believe that women of colour in Canada in the past two hundred years were often strangers to the wider white societies, men in their own communities, and even among themselves.At the same time, the contributors are cautious, and I would argue quite successful, to emphasize the inextricable intersections of the female subjects' ethnicity with the subjects' sex and class positions.The articles illustrate comprehensively ways in which women's positions as strangers in Canada were and remain fluid.They demonstrate how the subjects' marginalized and victimized realities are sometimes partnered with impulses of joy, community-building, and renewal in Canada's struggles with its "multicultural" identity and colonizing history of Aboriginal people in the past two centuries.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".