Colorism as Marriage Capital: Cross-Region Marriage Migration in India and Dark-Skinned Migrant Brides
Bibliographic record
Abstract
This article, based on original research from 57 villages in four provinces from North and East India, sheds light on a hitherto unexplored gendered impact of colorism in facilitating noncustomary cross-region marriage migrations in India. Within socioeconomically marginalized groups from India’s development peripheries, the hegemonic construct of fairness as “capital” conjoins with both regressive patriarchal gender norms governing marriage and female sexuality and the monetization of social relations, through dowry, to foreclose local marriage options for darker-hued women. This dispossession of matrimonial choice forces women to “voluntarily” accept marriage proposals from North Indian bachelors, who are themselves faced with a bride shortage in their own regions due to skewed sex ratios. These marriages condemn cross-region brides to new forms of gender subordination and skin-tone discrimination within the intimacy of their marriages, and in everyday relations with conjugal families, kin, and rural communities. Because of colorism, cross-region brides are exposed to caste-discriminatory exclusions and ethnocentric prejudice. Dark-skin shaming is a strategic ideological weapon employed to extract more labor from them. The article extends global scholarly discussion on the role of colorism in articulating new forms of gendered violence in dark-complexioned, poor rural women’s lives.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".