School trajectories of the second generation of Turkish immigrants in Sweden, Belgium, Netherlands, Austria, and Germany: The role of school systems
Bibliographic record
Abstract
In this article, we aim to explain the school careers of the second generation of Turkish immigrants in nine cities in five Western European countries and show the influence of the national school systems ranging from comprehensive to hierarchical tracking structures. We apply sequence analyses, optimal matching, and cluster analyses to define school trajectories complemented with propensity score matching to study the differences between young adults of different origin. Participants were 4516 young adults of Turkish second generation and native origin aged between 18 and 35. Findings show that the school system makes a difference for school careers: (1) in rigid systems with higher differentiation and early tracking, the gap between the second-generation and native school trajectories begins to unfold early in the school career; (2) in the rigid systems, the track in which students enter secondary education determine the routes they take as well as their final outcomes; and (3) more open systems allow for “second-chance” opportunities for immigrant students to improve their track placement. However, across school systems, second-generation youth follow more often non-academic or short school careers, while native youth follow academic careers. When individual and family background are controlled via propensity score matching, the ethnic gap is explained better in more stratified systems highlighting the important role of family background in more stratified school systems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".