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Record W2753881904 · doi:10.3138/jcfs.48.1.113

Understanding Happiness and Psychological Wellbeing Among Young Married Women in Rural India

2017· article· en· W2753881904 on OpenAlexvenueno aff
Sudeshna Ghosh, Subrata Lahiri, Nitin Datta

Bibliographic record

VenueJournal of Comparative Family Studies · 2017
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsHappinessWifeWest bengalPsychologyMarital statusAge at first marriageSocial psychologyDemographySocioeconomicsSociologyPopulationFertilityPolitical scienceLaw

Abstract

fetched live from OpenAlex

The practice of early marriage is deeply rooted in the socio-cultural structure and is well prevalent in rural India despite marriage-prohibiting laws. The paper examines happiness and psychological well-being and its influence by various aspects of marriage among young married women. Multistage sampling technique was used to interview 654 married women, 13-24 years in rural West Bengal. Mean age at marriage was found to be 16 years. One-third reported adjustment problem initially after marriage. Logistic regression analysis results showed marital happiness was positively influenced by less spousal difference in opinion; couple educational status, able to adjust marital problems and wish to have same life partner given an option of partner selection. No significant association was found between happiness and type of marriage and husband’s currently staying with wife.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.272
GPT teacher head0.476
Teacher spread0.204 · 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 source (direct Gemma or distilled Codex), 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

Citations10
Published2017
Admission routes1
Has abstractyes

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