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Record W3006789854 · doi:10.3138/jcfs.50.4.003

Variations in Marriage Squeeze by Region, Religion, and Caste in India

2019· article· en· W3006789854 on OpenAlexvenueno aff
Minakshi Vishwakarma, Chander Shekhar, Akhilesh Yadav

Bibliographic record

VenueJournal of Comparative Family Studies · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicDemographic Trends and Gender Preferences
Canadian institutionsnot available
Fundersnot available
KeywordsCasteDemographyPopulationArranged MarriageGeographyEndogamyScarcitySex ratioSociocultural evolutionSocioeconomicsSociologyGender studiesPolitical scienceEconomicsLaw

Abstract

fetched live from OpenAlex

Marriage squeeze is a demographic phenomenon underlining the asymmetry between the availability of potential brides and grooms in a population. Since mate selection is very specific and bound by religion, caste, and region in India, existing demographic and sociocultural variability reflects even more emphatically on marriage squeeze in these subgroups. The last round of the Indian census (2011) was used for this study. To capture the marriage squeeze, the study utilized two methods—Schoen’s method of two-sex life table and the sex ratio method. At present, India is experiencing a marriage squeeze for males in rural as well as urban areas. Differences have been observed in the tightness of marriage squeeze across different subgroups of the population. The tightness of the male marriage squeeze is greater among Hindus than Muslims in the age group 15–45. In terms of caste, the scheduled tribes are experiencing a higher scarcity of brides than “other” castes; conversely, scheduled caste brides are experiencing a scarcity of grooms. Across the states, a higher tightness of marriage squeeze among males is observed in Punjab, Uttar Pradesh, Mizoram, and Haryana compared to the rest of the states. The rapid changes that occurred in the sex ratio in the past few decades are visible in the initial two cohorts of marriageable age. Variations in marriage squeeze across different social groups are mainly driven by alterations in the natural sex ratio and the changing pattern of marriage in India.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.080
Threshold uncertainty score0.311

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.072
GPT teacher head0.364
Teacher spread0.292 · 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

Citations8
Published2019
Admission routes1
Has abstractyes

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