The problem of multilingualism and linguistic diversity in the European society
Bibliographic record
Abstract
The article investigates the phenomenon of multilingualism and linguistic diversity in Europe. Despite the challenges posed by differences in the implementation of policies and practices of multilingualism in different states, the comparative data, presented in this study, are a rich source for cross-national review. The author notes that together with such concepts as respect for the individual, tolerance, openness to other cultures, the linguistic diversity of folks in the European society is one of its key values. Therefore, the activities of the European Community are aimed at encouraging and supporting many languages. Obviously, the language policy in Ukraine should be conducted precisely in the light of the prospect of the European integration. Indeed, becoming a full member of the united Europe our state will be able to exist under the appropriate conditions. Together with the experts, we came to the conclusion that our country should make a decision that will correspond to its historical tradition, heritage, requirements of modernity and the will of the Ukrainian people. Either we will follow the example of countries that are officially monolingual, like France or the United Kingdom, or we will use the experience of officially multilingual states like Switzerland, Belgium, Canada, etc., or our people will choose their own way in the language policy.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".