Loyalty and patriotism: the role of Crimean Tatars in Ukraine’s nation-building project
Bibliographic record
Abstract
During the Revolution of Dignity and the annexation of Crimea in 2014, Crimean Tatars overwhelmingly expressed their loyalty and identification with the Ukrainian state. This article examines the factors that account for Crimean Tatars’ siding with Ukraine and interrogates the meaning of Ukrainian identity for their culturally, linguistically, and religiously distinct community. To do that, the author engages in a twofold approach of 1) macro-level, long-term historical examination of the relationship between Ukrainian state actors and Crimean Tatars, and 2) micro-level, sociological analysis of the contemporary relationship within Crimean Tatar society. Tracing the genealogy of encounters between Crimean Tatar and Ukrainian dissidents in the Soviet period, the author argues that their shared anti-Soviet outlook allowed the two nations to view each other as allies in independent Ukraine. Furthermore, the liberal, anti-colonial discourse shared widely among Crimean Tatars found its reflection in their newly embraced civic Ukrainian identity. Yet it is also important to take into account the internal struggle among Crimean Tatars during the annexation, which reflects the tensions, risks, and rewards that come with the choice of identification.
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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 | 0.017 | 0.013 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.006 |
| 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".