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Record W3015789897 · doi:10.1353/ces.2020.0006

Civic Engagement and Wellbeing Among Female Immigrants in Canada

2020· article· en· W3015789897 on OpenAlexvenueaboutno aff
Yiyan Li

Bibliographic record

VenueCanadian ethnic studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsnot available
Fundersnot available
KeywordsCivic engagementImmigrationSocial engagementPoliticsSociologyPublic engagementWork engagementPolitical scienceWork (physics)Public relationsSocial science

Abstract

fetched live from OpenAlex

Much international research has examined the various ways in which immigrant women engage in the new society and the relationships between these ways of engagement and their wellbeing. The present study explores various factors that influence immigrant women's levels of civic engagement and their effects on these women's subjective wellbeing. Using data from the 2008 General Social Survey (GSS) and a custom-designed Civic Engagement Index (CEI), a representative sample of 1,872 immigrant women was analyzed based on a variety of civic engagement indicators, such as volunteer work, donations, membership in organizations and associations, and political participation. Although immigrant women had low levels of civic engagement, they tended to become more familiar with and knowledgeable about civic life—thus increasing their civic engagement—the longer they resided in Canada. Building social networks through the labour market, educational institutions, and parenting activities also helped to improve civic engagement among immigrant women. In addition, increased levels of civic engagement were found to be positively associated with both life satisfaction and mental health, with socio-economic status playing a central role in this relationship. The findings of this study expand our understanding of the role of civic engagement in immigrant women's lives and can contribute to the development of policies that will promote the wellbeing of immigrant women.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score0.583

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 studies0.0000.000
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 head0.169
GPT teacher head0.345
Teacher spread0.176 · 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 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

Citations12
Published2020
Admission routes2
Has abstractyes

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