Les résultats économiques des réfugiés et réfugiées d’Europe de l’Est admis au Canada entre 1990 et 2007 : une analyse longitudinale par catégorie d’immigration et région d’origine
Bibliographic record
Abstract
Canada received more than 6 million immigrants from 1990 to 2007, of whom more than 500,000 were refugees fleeing the conflicts and persecution that plagued the world during that period. However, the labour market outcomes of Eastern European Refugees have not often been examined in detail. Using the Longitudinal Immigration Database (IMDB), this paper analyzes the early years of economic integration of Eastern European refugees admitted as permanent residents to Canada from 1990 to 2007, when they were aged 25 to 54. Particular attention is paid to Eastern European immigrants because Europeans are generally among those who demonstrate better economic outcomes in the country. Thus, this paper focuses on two key labour market indicators: employment rates and income levels. Labour market outcomes are then examined by immigration categories (government-assisted refugees, privately sponsored refugees and asylum seekers), region of birth, and gender. The results show that asylum seekers (both male and female) generally have an initial advantage on both economic indexes under analysis, but, relative to resettled refugees (those who are government or privately sponsored), this advantage fades over time. Additionally, we observe a significant variation in results depending on the region of origin and male refugees have higher economic participation than female refugees.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".