Barriers Faced by Immigrant Women Entering the Canadian Workforce- A case study of female students at Seneca College
Bibliographic record
Abstract
Newly released census data for the average number of landed immigrants in Canada is approximately 235,000 per year, dating back to the early 1990s. From July 2015 to July 2016, a total of 320,932 new immigrants landed in the country as permanent residents. From 2011 to 2016, Canada’s population grew about 5% with 66% of population growth being a result of a migratory increase and only 33% coming from a natural increase. Immigrants to Canada are one of the major drivers of economic well-being and innovation, and they have also provided much needed skilled labour to the labour force. Many research studies suggest that Canada needs larger number of immigrants to maintain its economic growth rate, support the ageing population and improve social prosperity. From year 2001 to 2006, the population of immigrant women grew by 14% versus that of Canadian born women which grew at 3.4%. Should current growth trends continue, by 2031 immigrant women will make up 27.4% of Canada’s female population. In light of the growing importance of immigrant women in Canada, it is imperative to understand and address the unique barriers these women face in assimilating into the labour force. This research paper will provide an understanding of the unique barriers faced by immigrant women seeking to assimilate into the Canadian labour force. Recognizing these barriers is the first step for educational institutions and settlement organizations to take when developing programs focused on addressing this issue.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".