Bilingualism and linguistic identity: Language planning strategies in multilingual Kazakhstan, Canada, and Belgium
Bibliographic record
Abstract
In an environment of concurrent trends toward globalization and regionalization, as well as the strengthening of processes associated with the reinforcement of national identity, the effective management of linguistic diversity becomes a key task for multilingual and multicultural states. These processes affect both indigenous titular and minority languages, as well as exogenous standard languages. Bilingualism, being the norm in such societies, is not only a linguistic phenomenon but also a powerful factor in shaping identity that requires thoughtful policy. The aim of this study is to analyze how bilingualism affects language identity and what the strategies for its regulation are in contexts with different sociolinguistic configurations of language situations. Based on a macro-sociolinguistic approach and a comparative analysis of the language policy models of Kazakhstan, Canada, and Belgium, the study shows that the success of measures to regulate and manage multilingualism directly correlates with the consideration of the specifics of the language situation. Primarily, this refers to parameters such as the type of territorial distribution of languages, the type of their standardization, the degree of autochthonous status, and its significance for the language community. The results demonstrate that the sustainability of multilingualism and the avoidance of conflict dynamics directly depend on systemic state support and management, the effective model of which can vary: asymmetric bilingualism in Kazakhstan, institutional in Canada, territorial in Belgium. The key conclusion is that the success of language policy is based on flexible and inclusive strategies that integrate education, media, and intercultural dialogue, where bilingualism is viewed as a resource for social development and integration, rather than as a problem.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".