Racialized Migrant Women in Canada: Essays on Health, Violence and Equity
Bibliographic record
Abstract
Introduction Vijay Agnew Part I: Immigrant Women and Violence Introduction The Complicity of the Public State in the Intimate Abuse of Immigrant Women Janet E. Mosher Violence in Immigrant Familes in Halifax Barbara Cottrell, Carmen Celina Moncayo, and Evangelia Tastoglou Part II: Immigrant Women and Health Introduction Gender, Migration and Health Arlen Bierman, Farah Ahmad, and Farah Mawani Policy (In)Action: Policy-Making, Health and Migrant Women Denise L. Spitzer Review of Health and Policy Research on Older Immigrants Ito Peng and Margot Lettener Reaching Out and Scaling Up: The Dynamics and Relevance of Migrant Women's Social Capital Bilkis Vissandje, Alisha Apale, and Saskia Wieringa Part III: Immigrant Women and Equity Introduction Immigrant Women and Earnings Equality in Canada Monica Boyd and Jessica Yiu Migrant Muslim Women's Intersts and the Case of Shari'a Tribunals in Ontario Annie Bunting and Shado Mokhtari Haitian-Canadians' Experiences of Racism in Quebec: A Postcolonial Feminist Perspective Louise Racine Challenging Gendered and Ethno-Racial Assumptions in Organizing for Housing Rights in Montreal Jill Hanley Conclusion List of Contributors
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.031 | 0.017 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".