Second generation adolescents' competencies and the role of integration policies
Bibliographic record
Abstract
Immigration into the OECD countries has seen a sharp increase since the middle of the 1980s, even if not at a constant rate. Integration policies are a fundamental tool to help the newly arrived to integrate and assimilate with the native population. While the literature on the immigrants' integration level is very rich for settlement countries (USA, Canada, Australia and New Zealand) and for the few European countries that have a long tradition of immigration (Germany, UK, France), very little is yet known about other European economies that have only recently become destination countries. Indeed, the availability of data has made difficult to carry out comparative analysis of the integration process of immigrants in most of the EU countries, particularly for the second-generation. This research wants to fill this gap, analysing the role of the socio-economic background in the educational outcome of immigrants. Furthermore, we demonstrate how the effect of the socio-economic background is more or less pronounced in different EU countries that adopt different integration policies and have different education systems. In this work, we concentrate on second-generation adolescents and compare their performances with that of native adolescents and with that of first generation adolescents. The chosen indicator is the score obtained in the 2012 PISA test by each student (native, first and second-generation immigrant) in reading. We compare the results obtained for each of the EU15 member states and for the settlement countries. The results, in line with the prevalent literature, show a strong impact of the socio-economic background on the immigrant adolescents' performances. The effect is weaker in those countries where the integration policies concern disadvantaged children since an early age.
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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 | 0.002 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".