Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".