Education systems, school segregation, and second-generation immigrants’ educational success: Evidence from a country-fixed effects approach using three waves of PISA
Bibliographic record
Abstract
Many countries are increasingly being challenged to integrate their growing immigrant populations. A major key to successful integration is the educational attainment of immigrant offspring. According to the results of comparative studies, second-generation immigrant students often lag behind their non-immigrant counterparts even though the host countries perform very differently with respect to the education of immigrant offspring. This study investigates how the interplay between the degrees of stratification and standardization in education systems and the degree of ethnic school segregation affects the performance gap between non-immigrant and second-generation immigrant students in member countries of the Organisation for Economic Co-operation and Development (OECD). Based on data from three waves of the OECD Programme for International Student Assessment (PISA) study (2003, 2006, and 2009), this article presents a country fixed effects approach to analyzing repeated cross-sectional data by investigating how changes in education policies and institutional contexts are associated with non-immigrant–immigrant reading performance gap. Between-school stratification was associated with lower performance of second-generation immigrants relative to native students, particularly when paired with ethnic school segregation, whereas within-school stratification (ability grouping) was associated with higher relative performance of the immigrant students. In addition, the non-native students benefited from less standardization of educational input, because performance gaps were smaller when a country’s educational resources were distributed unequally.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".