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Social Dimensions of Human Development: Trends of Union Dissolution in India

2025· book-chapter· en· W7116973095 on OpenAlexaff
Ankita Rajgarhia, Zakir Husain, Mousumi Dutta

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicFamily Dynamics and Relationships
Canadian institutionsHeritage College
Fundersnot available
KeywordsEmpowermentIndependence (probability theory)GlobalizationEuropean unionSocial changeMarital status

Abstract

fetched live from OpenAlex

Union dissolution, encompassing divorce and separation, is an integral aspect of social transformation, reflecting shifts in societal norms, gender roles, economic independence, and legal frameworks. In India, while marriage has traditionally been seen as a lifelong commitment, increasing urbanisation, economic independence of women, and legal reforms have contributed to shifting perceptions of marriage, leading to greater acceptance of dissolution. The incidence of divorce and separation in India has historically been low compared to Western societies. However, there has been a steady increase in marital dissolution as indicated in this exploratory study that impacts the women immensely. Women, particularly in middle age groups, are more vulnerable to dissolution, with separation rates significantly higher than divorce rates. Factors such as financial independence, domestic conflicts, changing social norms, and globalisation have influenced this trend. Union dissolution varies across regions, showing a distinct north–south divide, with southern states experiencing higher dissolution rates. Among religious groups, Christians have the highest dissolution rates, particularly in urban areas. Across all demographics, separation remains the dominant form of dissolution. While union dissolution is increasing in India, challenges such as social stigma, economic hardship, and legal battles persist. Addressing these concerns through legal reforms, social awareness, and economic empowerment is crucial for ensuring the well-being of individuals undergoing marital dissolution.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.716
Threshold uncertainty score0.456

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.036
GPT teacher head0.309
Teacher spread0.273 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations0
Published2025
Admission routes1
Has abstractyes

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