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Record W2186484889 · doi:10.3138/jcfs.36.4.567

Race and Independent Living among Elderly Brazilians Since 1980

2005· article· en· W2186484889 on OpenAlexvenueno aff
Susan Devos, Flávia Cristina Drumond Andrade

Bibliographic record

VenueJournal of Comparative Family Studies · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsnot available
Fundersnot available
KeywordsUrbanizationChinaLatent class modelRace (biology)IdeologyPoliticsGender studiesGender analysisGender roleDemographyGeographyPsychologySociologyPolitical scienceEconomic growth

Abstract

fetched live from OpenAlex

This article investigates gender differences in gender-role attitudes in two societies with the same cultural heritage and identifies factors differentiating patterns of women’s and men’s attitudes. The data were collected from two surveys conducted in Taiwan and coastal China during 1996 97, with a total of 2.801 and 2,907 completed interviews, respectively, used in the analysis. Latent Class Analysis revealed two similar latent forms of gender-role attitudes for both genders in the two societies. Most of the respondents, with more males than females and more coastal Chinese than Taiwanese, were classified as holding “traditional type” attitudes. The gender gap in traditional attitudes was larger in Taiwan than in coastal China. The factors differentiating women’s patterns of attitudes from men’s found in Taiwan support the perspectives of gendered self-interest and paternal role-model. Urbanization and cohort effects were more significant in coastal China, showing that political ideology and policy implementation in different stages and regions shape gender-role attitudes in different societies. This study supports the importance of incorporating gender analysis into interpretations of societal differences in the ways gender-role attitudes are structured.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.395
Threshold uncertainty score0.490

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 communication0.0000.001
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 head0.039
GPT teacher head0.353
Teacher spread0.313 · 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 designQualitative
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

Citations5
Published2005
Admission routes1
Has abstractyes

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