The implications of the Copenhagen political criteria on the language rights of the Kurds in Turkey /
Bibliographic record
Abstract
In recent years, the attention is being increasingly drawn to the role of the European Union on the development of minority rights in the candidate countries. The adoption of the Copenhagen political criteria, which also require "respect for and protection of minorities," as preconditions that applicants must have met before they could join the Union has inevitably led to some policy changes to the minorities in Eastern Europe. This policy shift is particularly directed at minority language rights, because one of the most important aspects of the protection of minorities is the recognition of their linguistic identity. The aim of this study is to explore to what extent this development has influenced the situation of language rights of the Kurds in Turkey. In order to answer this question, it first examines the relationship between the Copenhagen criteria and international and European standards protecting minority language rights. Secondly, considering those standards, it assesses the achievements and failures of the recent legislative amendments which are directed to bring the language rights of the Kurds within the line of the Copenhagen criteria. The case of Turkey reveals the vast potential of the European enlargement process on the development of minority language rights, but also its limits in situations where there is a lack of political will to respect and protect diversity.
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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 | 0.003 | 0.005 |
| 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 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".