Bibliographic record
Abstract
This study analyzes which party of the conflict was involved in the 2014 Maidan massacre in Ukraine.The massacre of Maidan protesters and the police on 20 February 2014 was a turning point in Ukrainian politics.This mass killing led to the overthrow of the Ukrainian government and spiraled into a civil war in Donbas, Russian military intervention in Crimea and Donbas, the Russian annexation of Crimea, and conflicts between Ukraine and Russia and between the West and Russia that Russia drastically escalated by launching its illegal invasion of Ukraine in February 2022.This article proposes and tests the moral hazard theory of the state repression backfire.Content analysis of synchronized videos, testimonies by several hundred witnesses, confessions by 14 self-admitted members of Maidan sniper groups, and bullet hole locations show that both the police and protesters were massacred by Maidan snipers located in Maidan-controlled buildings and areas.Content analysis of synchronized videos revealed that the specific time and direction of shooting by Berkut policemen, who were charged with the massacre, did not coincide with the killing of specific protesters.Testimonies by the absolute majority of wounded protesters and some 100 witnesses and forensic examinations by ballistic and medical experts for the Maidan massacre trial and investigation in Ukraine corroborate this.The article shows that the false-flag massacre was
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".