Measuring success: predictors of successful economic integration of resettled female refugees
Bibliographic record
Abstract
There is a growing political, academic and practical interest in refugee integration in Canada. The challenge, however, is that not much of the existing research focuses specifically on refugee women and their unique experiences beyond mental and physical health. My dissertation contributes to addressing this gap by examining their successes and challenges in the Canadian labour market. Using the 2016 version of the Longitudinal Immigration Database (IMDB), this dissertation addresses the question: What are the characteristics that predict economic success among refugee women in Canada? A secondary question asks, to what extent does arriving during an economic recession influence the income of refugee women? This dissertation uses Critical Race theory, Intersectional theory and Segmented Labour Market theory informed by a quantitative research design to address these questions. These theoretical perspectives help to understand the findings suggesting that the barriers in the Canadian labour market help to sustain existing racism, discrimination and inequality refugee women experience. The findings indicate that the level of education at arrival for refugee women in Canada varies. Based on the sample population in my dataset and existing research, there are large numbers of refugee women with low levels of education compared to those with graduate-level education and of those with university degrees. A significant number (62%), however, have skilled and technical education and work experiences prior to their arrival to Canada. In addition, the most dominant skill level among refugee women in Canada is elemental labour followed by intermediate labour and clerical skills. Very few refugee women (mainly those aged between 35 to 49 years) arrive in Canada with managerial and professional skills. Education is an important predictor of the employment income of refugee women in Canada according to the results of the multivariate analysis. The findings in this study, not surprisingly, reveal that refugee women with university degrees earn significantly more than those with a high school diploma or less. As time in Canada and education levels increase, so does the chances of earning an income that is higher than median employment income. Arriving during a recession (2008) does not seem to have an influence on their wages in the long-term. In the short term, however, there is a decline in wages and income three and six years after arrival for groups who arrived during the latest recession.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".