Bibliographic record
Abstract
Abstract Schools promote mobility by providing access to national languages and languages of wider communication, to knowledge and skills valued in the national and European economies, and to multicultural or cosmopolitan forms of identity. Ironically, however, mobility now poses unprecedented challenges for national school systems in EU member states, which were not designed to respond to the educational needs of large numbers of minority, migrant and refugee families speaking many different languages. Improving the trade-off between mobility and inclusion in these school systems implies improving access, participation and outcomes for these socially excluded populations. Although national education policies have a role to play in achieving this, it is the meso level of organization – school systems, teacher education programs, local and regional government, community associations and so on – where practical solutions need to be developed and implemented. This chapter addresses three important aspects of this meso level of capacity-building for linguistic inclusion in education: the building of local educational partnerships, the education of teachers, and the recognition, validation and assessment of community skills. Rather than focusing on language issues in isolation, the goal is to rethink and adapt well-established inclusion-oriented policy frameworks or initiatives in each of these areas. Overall, the analysis demonstrates that adapting national school systems to the needs of a mobile, multilingual Europe will depend on creative collaboration on the part of policy- and decision-makers at the meso level, as well as teacher trainers, student teachers, teachers, students and families, and community organisations.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.025 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".