School trajectories of the second generation of Turkish immigrants in Sweden, Belgium, Netherlands, Austria, and Germany: The role of school systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it