Bibliographic record
Abstract
The global landscape is dotted with border crossings that can be particularly perilous for displaced women with children in tow. These mothers are often described by their various legal statuses like refugee, migrant, immigrant, forced, or voluntary, but their lived experiences are more complex than a single label. Reclaiming Migrant Motherhood looks at literature, film, and original ethnographic research about the lived experiences of displaced mothers. This volume considers the context of the global refugee crisis, forced migration, and resettlement as backdrops for the representations and identity development of displaced women who mother. Situated within motherhood studies, this book is at the interdisciplinary intersection of literature, life writing, gender, (im)migration, refugee, and cultural studies. Contributors examine literary fiction, memoirs, and children’s literature by Ocean Vuong, Nadifa Mohamed, Laila Halaby, Susan Muaddi Darraj, Terry Farish, Thannha Lai, Bich Minh Nguyen, Julie Otsuka, V. V. Ganeshananthan, Shankari Chandran, and Mary Anne Mohanraj. The book also explores ethnographic research, creative writing, and film related to refugee studies. The border-crossings discussed in the volume are often physical, with stories from Afghanistan, Syria, Vietnam, Japan, Iraq, Canada, Greece, Somalia, Palestine, Sri Lanka, and America. The borders that displaced mothers face are examined through frameworks of postcolonialism, nationalism, feminism, and diaspora studies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".