THE RELATIONSHIP BETWEEN CIVIC AND ETHNIC NATIONALISM ACCORDING TO ULADZIMIR FOURS, AND INSTITUTIONS FOR ENFORCING LANGUAGE RIGHTS AS THEIR POINT OF CONVERGENCE
Bibliographic record
Abstract
The paper critically examines Uladzimir Fours’s approach to the relationship between civic and ethnic nationalism. As an illustration of their interplay, it highlights public institutions responsible for the protection of language rights and the implementation of language policy. The article references Belarusian initiatives aimed at institutionalizing the defense of linguistic rights — such as the proposed but ultimately unrealized Belarusian state department for language affairs. As practical examples, it discusses public bodies that uphold linguistic rights in post-colonial countries and territories formerly under British rule. These include the Ministry of National Co-existence, Dialogue, and Official Languages of Sri Lanka; language commissioners of Canada, the Republic of Ireland, and Wales; and the Scottish Gaelic Board, among others. The paper emphasizes how such institutions contribute to civic autonomy, which Fours identifies as a key component of nationalism. Activities that promote this autonomy include assisting individuals in defending their linguistic rights, supporting institutions in developing language schemes, and fostering public dialogue.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.032 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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".