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Record W2131648638 · doi:10.1086/668282

Status, Caste, and the Time Allocation of Women in Rural India

2012· article· en· W2131648638 on OpenAlexaff
Mukesh Eswaran, Bharat Ramaswami, Wilima Wadhwa

Bibliographic record

VenueEconomic Development and Cultural Change · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Labor, and Family Dynamics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCasteAutonomyHierarchyWork (physics)PovertyDemographic economicsTime allocationEconomicsDeveloping countrySurvey data collectionSociologyEconomic growthSocioeconomicsDevelopment economicsPolitical scienceSocial science

Abstract

fetched live from OpenAlex

We argue that women may be disinclined to participate in market work in the rural areas of India because of family status concerns in a culture that stigmatizes market work by married women. We set out a theoretical framework that offers predictions regarding the effects of caste-based status concerns on the time allocation of women. We then use the all-India National Sample Survey data for the year 2004–5 and the Time Use Survey for six states of India for the year 1998–99 to empirically test these hypotheses. After controlling for a host of correlates, we find that the ratio of women’s market work to men’s declines as we move up the caste hierarchy. This ratio falls as family wealth rises, and the decline is steeper for the higher castes. Finally, the effect on women’s market work of higher education is weaker for the higher castes. These findings lend support to our theory and to the view that, through its emphasis on family status, caste plays a pivotal role in undermining the autonomy of women. Our article has implications for how culture impinges on the rate at which poverty in developing countries can be reduced.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.026
GPT teacher head0.248
Teacher spread0.222 · 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

Citations205
Published2012
Admission routes1
Has abstractyes

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