THE RELATIONSHIP BETWEEN CIVIL AND ETHNIC NATIONALISM BY ULADZIMIR FOURS AND LANGUAGE RIGHTS’ INSTITUTIONS AS THEIR INTEGRATION
Bibliographic record
Abstract
The paper critically examines Vladimir Furs’s approach to the relationship between civil and ethnic nationalism. As an example of the combination of civil and ethnic nationalism, public institutions for the protection of language rights and implementation of language policy are demonstrated. The article mentions Belarusian projects in the field of institutionalization of linguistic rights’ defence, such as the Belarusian language state department — envisaged, yet remained unimplemented. As working examples of such institutions, the article presents official linguistic rights’ bodies in post-colonial countries and territories that were once parts of the British Empire. Examined are the Ministry of National Co-existence, Dialogue, and Official Languages of Sri Lanka, language commissioners of Canada, Republic of Ireland, and Wales, the Scottish Gaelic Board, etc. The paper focuses on how these institutions contribute to civic autonomy, which Vladimir Furs considers to be an important feature of nationalism. Among the organizations’ activities that foster autonomy are the following: helping people to defend their linguistic rights, assisting institutions in creating their language schemes, fostering public dialogue, etc.
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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 | 0.002 | 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 | 0.004 | 0.013 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".