What Shapes the Integration Trajectory of Refugee Students? A Comparative Policy Analysis in Two German States
Bibliographic record
Abstract
Enabling the successful integration of refugee students into the German schooling system poses a crucial challenge for the coming years. Drawing from the human rights frame-work of the Inter-agency Network for Education in Emergencies standards, we applied a rights-based approach to policy analysis on educational provisions for refugee students from 2012 to 2018. According to international and European law, Germany is obliged to grant similar access to education for nationals as well as refugee children and youth. In reality, the realization of educational rights varies from state to state. This will be highlighted and discussed in this article, using the example of two very different German states, Hamburg and Saxony. The sudden rise of numbers of refugees led only slowly to an increase in educational policy density and intensity on federal state and national levels in 2016 and 2017. We find that the differences in compulsory schooling, models of integration into schooling, and the asylum and settlement policies in both states shape the educational participation of refugee children and youths. Both states implemented parallel integration models that might bear risks of stigmatization and limit educational possibilities. However, transition and language support concepts in both contexts contain integrative phases offering language supports in the regular classrooms. Asylum policies and state-specific settlement policies have profound implications for the rights and access to education. Further, vocational education and training programs play a crucial role, especially in Saxony, to tackle demographic challenges. ****Note that the original pdf version of this article contained a small production error that has now been corrected.***
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".