Caste and Cross-region Marriages in Haryana, India: Experience of Dalit cross-region brides in Jat households
Bibliographic record
Abstract
Abstract This article, based on original research in 75 villages in the North Indian state of Haryana, examines long-distance marriages of its dominant-peasant caste men with low-caste (Dalit) women from other parts of India. The male marriage squeeze caused by the female deficit in Haryana has led to this breach in the rules of caste endogamy in matrimony. These marriages and the gender status of such Dalit brides are situated within the context of polarized caste relations, caste contestations, and caste violence against local Dalits in Haryana. Long-distance alliances, through fabricated, high-caste identities of the brides, tactically circumvent prohibitions on local inter-caste marriages and provide legitimacy to continued, local, unequal hierarchies of caste relations. Intersecting oppressions of caste, gender, and patriarchy exacerbate gender subordination within both the home and community for Dalit cross-region brides. Caste-exclusionary behaviours and discriminations are strategically employed to assert caste supremacy and subdue women's resistance. The caste stigmatization of these brides carries over to their children who face inter-generational discrimination in daily interactions and marriage prospects because of their ‘diluted’ Jat identity and low-caste status. The article provides examples of Dalit brides’ agency through resistance strategies.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".