Special Issue: Political and economic obstacles of minority language maintenance
Bibliographic record
Abstract
This special issue of the Journal on Ethnopolitics and Minority Issues in Europe (JEMIE) brings together some of the participants of the symposium Political and Economic Resources and Obstacles of Minority Language Maintenance organized by the Language Survival Network ‘POGA’ at Tallinn University, Estonia, in December 2010. More than 20 scholars representing linguistics, anthropology, social sciences and law participated in the symposium, to present papers and discuss questions related to minority language loss, maintenance and revitalization. The six case studies contained in this special issue look at different minorities and regions in the European Union, Russia and the US. The linguistic communities discussed are the Russian-, Võru/Seto- and Latgalian-speaking minorities of Estonia and Latvia; the Welsh- and Breton-speaking communities of the Celtic language; the Russian Finno-Ugrian people with regional autonomies; and the native American groups of the Delaware/Cherokee and the Oneida. The reader will find articles relating to interdisciplinary research approaches in and on minority languages and minority language communities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".