Cross-jurisdictional linguistic cooperation in multilingual federations
Bibliographic record
Abstract
Abstract Political federations or quasi-federations characterised by linguistic diversity have developed various strategies to strike a balance between mobility and inclusion of (internal and external) migrants. This chapter first looks at the comparative performance of linguistic management and coordination between the central state and federal entities, mainly comparing Canada and the US, while exploring possible comparisons with India, in order to provide the EU with examples of language policies in large economic and political unions. We show that the experimental potential of sub-state entities, the cooperation between the public and the private sector, and reciprocity among sub-units are key to achieving linguistic non domination ( Section 1 ). It then suggests mobility and inclusion equilibria via linguistic subsidiarity and reciprocity for the EU ( Section 2 ). It concludes by introducing a new tool, a ‘language passport’ we have called Linguapass ( Section 3 ). The expected benefits of Linguapass on an individual level are to recognise and document the linguistic skills of migrants in official and non-official languages and hence to facilitate their mobility and inclusion; on a collective level, commitment to equal and reciprocal accreditation and funding of Linguapass by the EU as a whole, as well as by European regions and some large existing language clusters, is a novel form of equitable and feasible language cooperation and coordination.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".