The changing geo-cultural space of the CIS countries in the 20-21st centuries (on the material of renaming cities)
Bibliographic record
Abstract
The article deals with the main cultural and geographical approaches to the study of renaming — critical-toponymic, palimpsest, geoconceptual. From the position of conceptualization of space, a typology of renaming geographical objects is proposed. Among the main motives for renaming are the following: status, national renaming, de-Sovietization and de-Russification of space, “new Russification” of space. On the basis of a large amount of factual material, the changes in the geocultural space of the Neighboring countries are investigated. At the same time, there are 3 groups of countries: countries with a transformed geocultural space, where renaming affected more than half of the studied objects, countries with a changed cultural and geographical space, where renaming covers from 25 to 50 % of the cities considered, and countries with a slightly changed geocultural space (renaming covers less than a quarter of the studied objects). A significant group of countries of the Near Abroad (Abkhazia, Azerbaijan, Moldova, Kazakhstan, Turkmenistan, South Ossetia) belongs to the countries with a transformed cultural space. These countries are characterized by the processes of revolutionary attitude to the Soviet heritage (with the exception of Abkhazia and South Ossetia) and active cultural and linguistic innovations. Two countries of the Near Abroad (Kyrgyzstan and Tajikistan) belong to the countries with a changed cultural space. A significant group of countries in the Near Abroad belongs to countries with poorly modified cultural space (renaming covers less than a quarter of the studied objects). These are Ukraine, Belarus, Latvia, Lithuania, Estonia, Armenia, Georgia and Uzbekistan. Some of these countries are characterized by a low rate of cultural and linguistic innovation. Others — the Baltic states, Armenia, and Georgia-experienced waves of renaming much earlier, in pre-Soviet or Soviet times.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".