Forced Migration in the Feminist Imagination
Bibliographic record
Abstract
Forced Migration in the Feminist Imagination explores how feminist acts of imaginative expression, community-building, scholarship, and activism create new possibilities for women experiencing forced migration in the twenty-first century. Drawing on literature, film, and art from a range of transnational contexts including Europe, the Middle East, Central America, Australia, and the Caribbean, this volume reveals the hitherto unrecognised networks of feminist alliance being formulated across borders, while reflecting carefully on the complex politics of cross-cultural feminist solidarity. The book presents a variety of cultural case-studies that each reveal a different context in which the transcultural feminist imagination can be seen to operate – from the ‘maternal feminism’ of literary journalism confronting the European ‘refugee crisis’ to Iran’s female film directors building creative collaborations with displaced Afghan women; and from artists employing sonic creativities in order to listen to women in U.K. and Australian detention, to LGBTQ+ poets and video artists articulating new forms of queer feminist community against the backdrop of the hostile environment. This is an essential read for scholars in Women’s and Gender Studies, Feminist and Postcolonial Literary and Cultural Studies, and Comparative Literary Studies, as well as for those operating in the fields of Gender and Development Studies and Forced Migration 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.038 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".