Ethno-Language Issue as a Source of Separatism and Instability in Ukraine
Bibliographic record
Abstract
The article analyzes the impact of ethno-linguistic policy on the separatism and political instability in Ukraine. The article examines the current provisions of the legislation of Ukraine on the development of language policy, as well as the provisions of the latest Law on the status of the state language in Ukraine.Ukraine has severalspecific features from the linguistic point of view, they are: bilingualism, uneven distribution of Russian and Ukrainian languages on the territory of the country and in different sectors of the social sphere, as well as ethno-linguistic, social and socio-cultural polarization of the Western, Central and South-Eastern parts of the country. The Ukrainian language was recognized as the state languagein Ukraine in 1989. This preceded the signing of the Declaration on the State Independence of Ukraine in 1991. From that moment on, the Ukrainian language is considered a symbol of the new Ukraine. Raising the status of the Ukrainian language has become one of the central issues in the process of building an independent state. The UN Security Council discussion in July 2019 on the language policy in Ukraine showed the world community's concern over the problem of ensuring the rights and freedoms of citizens and national minorities in Ukraine in connection with the adoption of the Law on the legal status of the state Ukrainian language and its use in education and public life. The analysis focuses on the trends in the development of language policy, which is the source of aggravation of social processes in the form of separatism and destabilization of modern Ukraine and attempts of its modern political regime to finally complete the reorientation from Russia to the West at the legislative language level.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".