What Do We Do Now? - Qualitative Research on Immigrant Women’s Access to Skills Training Provided by the Public Service in Canada
Bibliographic record
Abstract
Women worldwide choose to immigrate to Canada inspired by other immigrants' experiences and immigration news. There are many reasons why women choose to emigrate; most are following a promise of socio-economic improvement, but for some, that promise is not easily realized. There is a need to understand what barriers keep newcomer women from socio-economic insertion once they have settled in Canada. This research aims to explore the services provided by the public sector in Canada to immigrant women, to support their skills development for successful socio-economic integration. The study investigates persistent barriers experienced by immigrant women in the settlement process that remain despite engaging with the available public aid. The research follows a framework analysis method which facilitates discovery and highlight themes from qualitative data on available research on immigration, socio-economic insertion, and skill development topics in Canada. Overall, this research aims to build awareness of immigrant women’s needs and their access to public resources intended to facilitate their effective socio-economic integration. The framework analysis on existing research allows for a mapping of themes found in articles related to gender, immigration, socio-economic insertion, and skill development, documenting systemic barriers impeding the settlement of immigrant women. The analysis of the data outlines the concept of the survival job (a job with the sole intention of providing quick income to subsist), while exposing the limited options public service programs have for high-skilled women and mothers. The research also uncovers ways in which family responsibilities felt upon arrival have a significant impact on both socio-economic integration and mental health. This research is important in that it provides findings relevant to policy makers, by identifying and analyzing gender-specific barriers present during the immigration process. This study will contribute to the body of knowledge on Canadian immigration and the role of Canadian public services, like public libraries and community centres, in providing sufficient and efficient settlement support to immigrants.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.030 | 0.013 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".