A tale of two regions: geopolitics, identities, narratives, and conflict in Kharkiv and the Donbas
Bibliographic record
Abstract
In 2014 Russia occupied and then annexed the Ukrainian region of Crimea, and subsequently incited and later directly supported a rebellion in southeastern Ukraine, ostensibly in both cases to protect the Russian-speaking population. Although the Crimean gambit was quickly resolved in Russia’s favor, at least on the ground, the fighting in the Donbas region of eastern Ukraine continues with huge loss of life, well over 2 million internally displaced persons, and massive damage to infrastructure. On the other hand, in the neighboring Kharkiv region, the population remained loyal to the Ukrainian state and Russian incitements to rebellion were rebuffed. This paper delves deeper into the mindset of the residents of eastern Ukraine to ascertain why support for Russia differs between these two regions. It focuses on the identities, memories, and narratives of the main groups of residents inhabiting the Donbas and Kharkiv Oblast. Then it compares the attributes of these main groups to each other to illustrate their differences. It characterizes the geopolitical narratives promoted by Russia to generate support for its actions to re-construct the Russian geostrategic area of control and demonstrates where and with which group these emotive narratives were successful and where and why they failed.
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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 | 0.001 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".