Immigrant Status, Early Skill Development, and Postsecondary Participation: A Comparison of Canada and Switzerland
Bibliographic record
Abstract
This paper examines differences in postsecondary-participation rates between students with and without immigrant backgrounds in Switzerland and Canada. For both countries, a rich set of longitudinal data, including family background, family aspirations regarding postsecondary education, and students' secondary-school performance as measured by Programme for International Student Assessment (PISA) scores, are used to explain these differences. Two groups are analyzed: all 15-year-old students; and all low-performing 15-year-old secondaryschool students. The results suggest that the gap in postsecondary participation between students with and without immigrant backgrounds, and its determinants, differs significantly between the two countries. This gap also differs significantly by students' source region background. In Canada, students with immigrant backgrounds who are low performers in secondary school have surprisingly high rates of postsecondary participation, particularly if they have an Asian background. In Switzerland, postsecondary participation among low performers in secondary school is much lower, whether they have an immigrant background or not. Possible reasons for these inter-country differences are discussed, including differences in the immigration and education systems as well as differences in the distribution of immigrants by source region. Related studies on immigration and education and training from the Social Analysis Division can be found at Update on Social Analysis Research.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".