Liberal Pluralism and Multiculturalism in Central and Eastern Europe (W. Kymlicka Views’ Analysis)
Bibliographic record
Abstract
Will Kymlicka is widely known in the world for the research in the field of the theoretical principles of liberal pluralism and justification of the policy of multiculturalism. In his scientific work, he pays attention not onlyto his native Canada but also draws attention to other regions of the world, including Central and Eastern Europe. The scientist asks whether the export of Western model of liberal pluralism and multiculturalism policies available in the region? Are Western models of multiculturalism and minority rights relevant for the post-Communist countries of Central and Eastern Europe? In the following article, we analyze Will Kymlicka’s views on this issue. Kymlicka explains why conventional ways of distinguishing between ethnic relations in the East and West do not help in understanding or responding to ethnic conflicts in the post-Communist world. He argues why the states of Central and Eastern Europe are not inherent in the territorial autonomy in their state building. He also argues why federalism as a form of government is not the solution of interethnic interaction’s problems. In addition, Will Kymlicka tries to highlight the unique characteristics of the region, which do not suggest the possibility of the introduction of liberal pluralism and multiculturalism in Central and Eastern Europe in the near future. Keywords: Liberal pluralism, multiculturalism, territorial autonomy, federalism, minorities
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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 | 0.001 |
| 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 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".