Contested Frontiers: Borders and Border Spaces in the South Caucasus from the Second Half of the 19th Century to the 1920s
Bibliographic record
Abstract
A closer look at the 19th century ethnographic maps of the Caucasus reveals the demographic diversity of the region at the crossroads of three empires: the Persian, the Ottoman, and the Russian. To consolidate their power in this peripheral region, these empires, and later the Soviet authorities, experimented with various scenarios of resettlement, making the region an imperial “laboratory” with massive border shifts. This article discusses the processes of border development in the South Caucasus, beginning with the integration of this region into the Russian Empire in the second half of the 19th century and continuing until Sovietization in the early 1920s. During this period, the borders in this region were particularly characterized by constant discourses, territorial claims, identity struggles, and ethnic divisions. The article considers the emergence and function of borders and border spaces from the perspective of their temporal evolution and analyses their mutability over time in an era marked by wars, revolutions, conflicts, and political upheavals. The aim is to provide a better understanding of why borders, whose meaning had diminished almost to insignificance during the Soviet period, became subjects of conflict again, turning them into sites of unpredictable aggression. Keywords: Armenia; Azerbaijan; war; contested borders; conflict; territoriality.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".