Soviet and Russian Regimes of Spatial Inscription: A Critical Analysis of Indigenous versus Official Place Names on Maps in Siberia, 1920s–2000s
Bibliographic record
Abstract
This article discusses Indigenous Evenki place names (hydronyms) on official topographic maps and handwritten sketches in a broader context of Soviet and Russian regimes of spatial inscription and toponymic policies and their legacies. The early Soviet policy of korenizatsia (indigenization) facilitated the incorporation of Indigenous place names into official nomenclature. As a result, a large number of Indigenous toponyms appeared on official maps. The aim of this article is to examine the evolving relationships between Indigenous place names and official place names of Indigenous origin across three historical periods (the 1920s, the 1950s, and the contemporary era) and in two politically distinct settings, the Republic of Sakha (Yakutia) and the Amur region. The data sets for this research were extracted from official maps and Evenki archival sketch maps collected by Glafira Vasilevich in Siberia between the 1920s and the 1960s. Using Quantum GIS in conjunction with ethnographic fieldwork, statistical analysis, and qualitative linguistic assessment, this research further investigates the evolution of Indigenous names on maps, focusing on the most characteristic changes in their inscription over the past century. This article concludes by highlighting the power dynamics between different Indigenous place-naming traditions in Siberia, where various Indigenous communities have historically distinct opportunities to influence toponymic policies.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 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".