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Record W4291633005 · doi:10.1177/10778012221097142

Adolescent Girls and Their Family Members’ Attitudes Around Gendered Power Inequity and Associations with Future Aspirations in Karnataka, India

2022· article· en· W4291633005 on OpenAlexafffund
Kalysha Closson, Ravi Prakash, Prakash Javalkar, Tara Beattie, Raghavendra Thalinja, Martine Collumbien, Satyanarayana Ramanaik, Shajy Isac, Charlotte Watts, Stephen Moses, Mitzy Gafos, Lori Heise, Marissa Becker, Parinita Bhattacharjee

Bibliographic record

VenueViolence Against Women · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicGender Roles and Identity Studies
Canadian institutionsUniversity of ManitobaUniversity of British Columbia
FundersViiV HealthcareDepartment for International DevelopmentDepartment for International Development, UK GovernmentGovernment of the United KingdomUniversity of Manitoba
KeywordsPromotion (chess)Logistic regressionPsychologyGender equalityInequalitySocial psychologyDemographyDevelopmental psychologyGender studiesSociologyPolitical scienceMedicinePolitics

Abstract

fetched live from OpenAlex

Intergenerational differences in inequitable gender attitudes may influence developmental outcomes, including education. In rural Karnataka, India, we examined the extent of intergenerational (adolescent girls [AGs] vs. older generation family members) dis/agreement to attitudes around gendered power inequities, including gender roles and violence against women (VAW). Unadjusted and adjusted logistic regression examined associations between intergenerational dis/agreement to attitude statements and AGs' future educational and career aspirations. Of 2,457 AGs, 90.9% had a matched family member (55% mothers). While traditional gender roles were promoted intergenerationally, more AGs supported VAW than family members. In adjusted models, discordant promotion of traditional gender roles and concordant disapproval of VAW were associated with greater aspirations. Results highlight the need for family-level programming promoting positive modeling of gender-equitable attitudes.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.204
Threshold uncertainty score1.000

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 studies0.0010.000
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 head0.027
GPT teacher head0.264
Teacher spread0.238 · 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.

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

Citations2
Published2022
Admission routes2
Has abstractyes

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