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Record W2230584902 · doi:10.3138/jcfs.44.2.193

Exploring the Myth of Mixed Marriages in India: Evidence from a Nation-wide Survey

2013· article· en· W2230584902 on OpenAlexvenueno aff
Srinivas Goli, Deepti Singh, T. V. Sekher

Bibliographic record

VenueJournal of Comparative Family Studies · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicDemographic Trends and Gender Preferences
Canadian institutionsnot available
Fundersnot available
KeywordsCasteEndogamySpouseHonourContext (archaeology)Survey data collectionArranged MarriageSociologyDemographic economicsGeographyGender studiesDemographyPopulationPolitical scienceEconomicsLaw

Abstract

fetched live from OpenAlex

Marriages in India are essentially endogamous in nature. The influences of western education and socio-economic transformations have led to enormous change in the existing pattern of choosing one’s life partner and marriage practices in India. For the first time, this paper presents a comprehensive empirical assessment of the extent of mixed marriages by analyzing nationally representative data from the Indian Human Development Survey (IHDS, 2005). We consider mixed marriages in the following key aspects: Inter-caste marriage, Inter-religious marriage and Inter-economic group marriage (Inter-class marriage). The trend analyses reveal that the proportion of inter-caste and interreligious marriages has doubled in the last two and half decades. With the exception of interclass marriages, the absolute level of mixed marriages is still exceedingly small. Besides, there are substantial variations across the states. Regression analyses show a significant socioeconomic differential in the occurrence of mixed marriages. The study reveals that a very few women have the freedom to choose their spouse. These findings assume importance in the context of an increasing number of ‘honour killings’ in India in the recent years.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.885

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.565
GPT teacher head0.415
Teacher spread0.151 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2013
Admission routes1
Has abstractyes

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