Bounded Inclusion: Race, Migration and the Making of the European Educational Space
Bibliographic record
Abstract
The creation of “European educational space” is one of the objectives of the European Union’s (EU) cultural policy. This paper examines an overlooked contradiction within the European educational space discourse, namely the tension between its stated goals of creating a new European identity based on common cultural heritage and its reliance on intercultural education’s ideas of trans‐ethnic identities to address the challenges of immigrants’ integration. Relying on the insights of critical race theory, the paper argues that the key assumptions behind the European educational space and intercultural education, far from being contradictory, are interconnected insofar as intercultural pedagogy informs the tropes of “migrants,” “integration,” and “multiculturalism” that are at the core of the European dimension of education’s discourse. The paper argues that these tropes are part of an evolving discourse about immigrant education that allows the EU to maintain a facade of multicultural benevolence while perpetuating a differential inclusion of EU and non‐EU migrants in Europe. To support these claims, the paper critically examines the evolution of the discourse surrounding migration and integration in the EU, focusing on the main policy initiatives on immigrant youth education elaborated from the 1970s onwards.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.047 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".