Toponymic Politics and the Symbolic Landscapes of Minsk, Belarus
Bibliographic record
Abstract
Recently, within the theoretical and methodological framework of critical human geogra-phy, the main focus of the toponymic research has been redirected from the traditional linguistic and socio-onomastic methods towards a critical analysis of the spatial politics of naming and the studies of the socio-political role of the place names as the components of the symbolic landscape. The toponymic politics of (re)naming the streets and other elements of the urban landscape has been a valuable tool for the political regimes to legitimate their symbolic power. This paper aims to analyze the relationships between the political power, the toponymic practices, and the symbolic landscapes on the example of the eclectic topo-nymic space of the city of Minsk, Belarus, from a semiotic perspective through the prism of the critical place names studies approach and the theoretical concept of toponymic identi-ty. Using cartographic and archival research, on-site urban observations as well as com-parative analysis, the in-depth case study reveals that the toponymic system of the Belarus-ian capital city consists of several elements which connect to an assortment of the symbolic spatial strategies of nation-building adopted by the governing authorities. The findings indicate that the urban toponymic landscape and the toponymic identities of the city of Minsk are symbolically motivated, and the heterogeneity of the contemporary urban topo-nymic system reflects actual political agendas of the past and current political regimes.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 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".