Citizenship and Employment: Comparing Two Cool Countries
Bibliographic record
Abstract
Over the last decades, both Canada and Sweden have liberalized citizenship regulations for permanent residents. During the same period, immigration patterns by country of birth have changed substantially, with an increasing number of immigrants arriving from non-western countries. The aim of this paper is to explore the link between citizenship and employment probabilities for immigrants in both countries, controlling for a range of demographic, human capital, and municipal characteristics such as city and co-ethnic population size. We use data from the 2006 Canadian census and Swedish register data (STATIV) for the year 2006. Both STATIV and the Census, include similar sets of demographic, socio-economic and immigrant specific. We use instrumental variable regression to examine the 'clean' impact of citizenship acquisition and the size of the co-immigrant population on the probability of being employed in both countries. We find that citizenship acquisition has a positive influence on employment probabilities in both Canada and Sweden. The size of the co-ethnic population has a positive impact for many immigrant groups--as the co-ethnic population increases, the probability of being employed also increases.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| 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".