How far is too far? Circassian ethnic mobilization and the redrawing of internal borders in the North Caucasus
Bibliographic record
Abstract
The idea of redrawing the borders between the republics of the region remains a topic of discussion beyond its borders. While the Kremlin de facto makes the subject of territorial changes in the North Caucasus taboo, the processes related to the rise of ethnic selfconsciousness in ethnic republics hardly stopped. The Syrian crisis, which gave Russia a much-celebrated diplomatic victory, threatens its territorial integrity because Moscow’s mishandling of the Circassian issue is radicalizing the Circassian communities of the North Caucasus. Drawing on the dynamics of ethnic mobilization among Circassians, the paper argues that this process may result in the most dangerous consequences of the Kremlin’s policies based on the ancient imperial principle of “divide and rule” — redrawing the administrative map of the entire region. The paper concludes that even though Moscow pretends that the situation is under control, a shift which consequences are hard to predict is already happening. One of them is that the demand for an increased congruency between Russia’s ethnic and administrative borders becomes politically salient; and a protrusion in the battle line becomes more prominent with each passing day.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 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".