Im/mobility as a form of gender-based violence: the case of transnationally abandoned wives in India
Bibliographic record
Abstract
Transnational marriage abandonment (TMA) of women is a growing form of violence reported across India and South Asia. The spouse, most commonly a husband, lives and works in a foreign country and exploits the advantages derived from his citizenship or visa status to exercise coercion and control over the immigrating wife. TMA takes different forms, including when a woman is left behind with the in-laws while waiting for the husband to provide visa sponsorship for her migration. Such women are vulnerable to financial precarity, isolation and domestic violence from in-laws, may be dispossessed from their marital home and served with ex parte divorces. Drawing on life-history interviews with 35 ‘never-migrant’ women conducted between 2013 and 2016, and subsequent policy and legal developments in India and the UK, this article seeks to unpack the gendered dimensions of im/mobility within TMA. Women’s immobilisation results from state migration policies, legal obstacles, patriarchal socio-cultural norms and purposive actions by husbands and their families to perpetually defer visa sponsorship and extract labour and/or money from women. Our findings indicate that immobilisation is a key facet of violence against women and legal responses to TMA must utilise a gender-based violence framework that can incorporate immobilised ‘never-migrant’ women.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".