Changes in the Educational Achievement of Immigrant Youth in Western Societies: The Contextual Effects of National (Educational) Policies
Bibliographic record
Abstract
Prior research has identified a large number of factors that explain differences in scholastic achievement. However, analyses of the educational performance of students from immigrant families and possible changes in these performances over time remain quite scarce. The present article addresses this shortcoming by means of a multilevel analysis of 24 Western countries based on PISA data from 2000 to 2012. Since the relevance of individual explanatory factors and school characteristics has been repeatedly shown, we focus on structural effects at the level of national policies. Our results show that the disparities in educational success between native and immigrant youth declined between 2000 and 2012 in several countries under study, especially in Central Europe. Nevertheless, there still are marked and relatively stable differences between various types of immigrant societies (e.g. traditional immigration countries like Canada vs. Continental European Countries like Germany). Analysing macro-level factors we observe stability as well as change: On the one hand, the positive effect of the quality of the educational system for immigrant students’ educational achievement remains stable. On the other hand, negative effects of the stratification of the educational system diminished, while educational expenditures gained influence over the last decade.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".