Analyzing the Impact of Socio-Demographic Factors, Linguistic Factors, and Level of Education on Immigrants’ Economic Integration in Canada
Bibliographic record
Abstract
This study undertakes a critical examination of the intersectionality of socio-demographic factors, linguistic proficiency, and educational attainment on the economic integration of immigrants in Canada, grounded in the theoretical framework of critical race theory. Through empirical analysis, this research investigates the mediating role of linguistic and educational factors in shaping the employment outcomes of immigrant populations, providing valuable insights for policymakers, researchers, and practitioners seeking to promote more effective integration strategies and address the disparities between immigrant groups and the native-born population. By utilizing data from the Longitudinal Immigration Database (IMDB) and employing a linear regression analysis model, this study examines the complex relationships between socio-demographic factors, linguistic proficiency and the economic integration of immigrants in Canada. The findings of this study highlight the significant impact of linguistic and educational factors on the economic integration of immigrants, underscoring the need for targeted language training programs and policies that cater to the diverse educational needs of immigrant groups, thereby promoting greater economic inclusion and social mobility for all immigrant groups.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".