Bibliographic record
Abstract
Abstract This chapter examines the role of separatists, the Yanukovych government, the Maidan opposition, and the Maidan government, far-right organizations, Russia, the United States, and the EU in the conflict in Donbas. It uses a specially commissioned survey by the Kyiv International Institute of Sociology (KIIS) in 2014 to analyze public support for separatism in Donbas, compared to other regions of Ukraine, and the major factors which affect such support. It concludes that all these actors contributed in various ways to the conflict in Donbas, which involved both a civil war and direct Russian military interventions in August 2014 and January–February 2015. The chapter links the origins of this conflict to Euromaidan, specifically, the Ukrainian government overthrow by means of the Maidan massacre and the secession and Russia’s annexation of Crimea. The KIIS survey shows that support for separatism was much stronger in Donbas compared to other regions, with the exception of Crimea. The Donbas war escalated into the Russia-Ukraine war as result of the Russian invasion of Ukraine in February 2022.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".