Latinization of the Language Under the Leadership of Turkey as a Tool for the Formation of a New Identity for the Turkic Post-Soviet States
Bibliographic record
Abstract
The study examines the issue of creating a common Turkic alphabet through the prism of Turkey’s interests in its attempts to form a new identity for the Turkic states. It is noted that the collapse of the Soviet Union and the formation of five new Turkic states were positively received in Turkey and gave impetus to the activation of pan-Turkic ideas. Thus, one of the aspects of Ankara’s humanitarian interaction with the newly formed countries was cooperation within the framework of linguistic reforms, where Turkey actively advocated for the Latinization of the alphabet of the Turkic states. The authors analyze the phenomenon of language in constructing the identity of states. Turkey, which has experience in radical linguistic reforms, recognizes the potential of such transformations in the Turkic space. Ankara seeks to lead this process in order to promote its own logics and narratives, which should ultimately lead to the adoption of the Turkish language as a single language for the Turks. It is noted that with the reformatting of the Organization of Turkic States (OTS), there has been a tendency to return to the agenda of discussions of a single alphabet for the Turkic peoples. Ankara promotes this topic at expert seminars, CTG meetings, etc. It is concluded that Turkey’s strategy is aimed at the long term and the lack of quick results in this area does not indicate its failure. Ankara systematically takes up the entire spectrum of interaction with Turkic partners, creating the foundation for future integration.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".