Three essays on the labour market integration of immigrants in Canada
Bibliographic record
Abstract
The first chapter “Family Immigration Policy and Women’s Employment” co-authored with Dr. Janice Compton examined the impact of a change in Canadian immigration policy on women’s employment. We use the Canadian Longitudinal Administrative Database (LAD) and a triple difference model to estimate the impact of a policy shift away from family-based immigration on the employment of immigrant women previously arrived. The results indicate that the reduction in family class immigration resulted in participation rates for immigrant mothers with young children that were 2.4 to 14.8 percentage points lower than expected. The second chapter “Internal Migration and Economic Outcomes: Evidence Among Young Adult Immigrants’’ employs the Canadian Longitudinal Immigration Database (IMDB) to estimate the impact of parental internal migration on the economic outcomes of young adult immigrants. I use Heckman's two-stage selection model and a linear probability model to estimate the coefficients. The results show that internal migration reduces employment earnings by 8.9 percentage change and increases the probability of low-income status by 3.5 percentage points among young adults whose parents entered the country through the refugee program, compared to those who entered through the economic class program. The final chapter “Requirements for Credential Recognition and Economic Outcomes of International Medical Graduates’’ employs information and data sources from the websites of the Colleges of Physicians and Surgeons of Canada, email correspondences, and Statistics Canada’s Longitudinal Administrative Database (LAD) to address two research questions on IMGs in Canada. A review of medical regulations for IMGs across the ten provinces in Canada suggests a protectionist motive behind these regulations. The second question examines the economic outcomes of immigrant IMGs in Canada. Analysis of the trends in the growth rate of employment earnings of immigrant IMGs from the first to the twentieth year in Canada shows an overall upward trend (except New Brunswick and Manitoba), with occasional fluctuations and periods of both increase and decrease. Regression results indicate a correlation between the province of residence and both the probability of positive employment income and employment earnings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".