The cultural politics of reproduction : migration, health and family making
Bibliographic record
Abstract
Acknowledgments List of Contributors Introduction Migration and the Politics of Reproduction and Health: Tracking Global Flows through Ethnography Sunil K. Khanna and Maya Unnithan-Kumar Chapter 1. Migration, Belonging and the Body that Births: Pakistani Women in Britain Kaveri Harris Qureshi Chapter 2. To Be or Not To Be?: Cape Verdean Student Mothers in Portugal Elizabeth P. Challinor Chapter 3. 'Good Women Stay at Home. Bad Women Go Everywhere': Agency, Sexuality and Self in Sri Lankan Migrant Narratives Sajida Z. Ally Chapter 4. 'No That's not a Religious Thing, That's a Cultural Thing': Culture in the Provision of Health Services for Bangladeshi Mothers in East London Laura Griffith Chapter 5. Health Inequalities and Perceptions of Place: Migrant Mothers' Accounts of Birth and Loss in Northwest India Maya Unnithan-Kumar Chapter 6. Acculturation and Experiences of Postpartum Depression amongst Immigrant Mothers Mirabelle E. Fernandes-Paul Chapter 7. 'A Mother who Stays but Cannot Provide is not as Good': Migrant Mothers in Hanoi, Vietnam Catherine Locke, Nguyen Thi Ngan Hoa and Nguyen Thi Thanh Tam Chapter 8. 'A City-Walla Prefers a Small Family': Son Preference and Sex Selection among Punjabi Migrant Families in Urban India Sunil K. Khanna Chapter 9. Restoring the Connection: Aboriginal Midwifery and Relocation for Childbirth in First Nation Communities in Canada Rachel Olson
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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 | 0.002 | 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 | 0.006 | 0.012 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".