Canadian Immigrant Youth and the Education-Employment Nexus
Bibliographic record
Abstract
Canada’s population of immigrant youth between the ages of 15 and 35 is approaching 3 million and growing rapidly. Youth are critical to Canada’s goal of recruiting immigrants to expand the economy, but there is insufficient information about their school and work experiences and inadequate support to ensure their successful integration into the workforce. This literature review investigates the connection between education and work for Canadian immigrant youth. It documents obstacles in the form of underfunded settlement services, lack of diversity in the school curriculum, inadequate English-language instruction at all levels of schooling, racially and ethnically biased streaming of students into the lowest educational track in high school, rejection of foreign school transcripts and work credentials, employers’ prejudice and discrimination, and workplace exploitation. The number and magnitude of these systemic impediments create significant obstacles for immigrant youth. A major cause of these issues is insufficient funding for immigrant services under neoliberal economic policies. The outcomes for immigrant youth include failure to finish secondary and postsecondary education, a long-term cycle of employment in low-skill, low-wage jobs, and socioeconomic hardship such as poverty and homelessness. The authors call for greater attention to this critical population and make nine recommendations that would contribute to solutions in each major issue area impacting the education of Canadian immigrant youth and their entry into the workforce.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".