The Impact of Source-Country Gender Inequality on the Acculturation, Structural Integration and Identification of Immigrants in Canada
Bibliographic record
Abstract
Many immigrants arrive in Canada from countries with different degrees of gender inequality. While Canada has relatively high levels of gender equality, many immigrant-origin countries are characterized by high levels of inequality between men and women. Studies show that source-country gender inequality negatively impacts immigrant women's socioeconomic outcomes in the host society. However, little is known about how source-country gender inequality impacts social aspects of immigrant adjustment in Canada. This dissertation examines how source-country gender inequality impacts acculturation, structural integration and identification. My analyses of data from the Ethnic Diversity Survey and General Social Surveys find that source-country gender inequality can benefit identification when measured by sense of belonging to Canada. In other cases, it can be a barrier when acculturation is measured by financial decision-making. Further, source-country gender inequality can have little impact on the structural integration of immigrants when measured by sport participation. The results suggest that source-country gender inequality affects immigrant men and women in complex and multifaceted ways.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 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) | 0.004 | 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".