Cross-region migration of brides and gender relations in a daughter deficit context
Bibliographic record
Abstract
In recent years there is a growing interest in the phenomenon of across-region marriages and the wellbeing of women in such marriages in female deficit regions in countries such as India and China. Several men in district Namakkal in the south Indian state of Tamil Nadu which has had a long history of daughter elimination are currently experiencing a delay in marriage due to female deficit, educational and occupational incompatibility, and women’s changing marriage preferences, with many bringing brides from outside the region. Women who are priced out of the marriage market in Kerala, a state where women occupy a relatively better status, are also marrying these men. Based on ethnographic fieldwork, this paper argues that the experiences of cross-region brides are not idiosyncratic or ad hoc but are shaped by and embedded within the intersection of caste, class, gender and kinship. In particular the gender contexts in both the place of origin and the place women are married into, affect their wellbeing. While such unions have the potential to create a win-win situation for both parties and challenge practices such as caste endogamy and dowry, they also reinforce unequal gender norms that maintain male privilege.
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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 | 0.002 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".