Bibliographic record
Abstract
Since the 2013-14 Euromaidan protest movement and the ousting of the pro-Russian President Viktor Yanukovych and his administration, Ukraine has been embroiled in a political crisis both domestically and with its neighbour, Russia. Namely, the Ukrainian state has lost the Crimean Peninsula to a Russian military incursion and remains engaged in the local Donbas War against pro-Russian insurrectionists. Alongside these events, Russian President Vladimir Putin’s administration has been waging an information war against Ukraine through its employment of Russian state media. In doing so, Russian state media has revived and internationalized Soviet historical narratives of the Great Patriotic War, discrediting the present Ukrainian state by associating it with historic examples of fascism. Thus, this paper argues that contemporary Russian attempts to reframe Ukrainian national history along a Russo-Soviet narrative, without consideration for the more authentically Ukrainian nationalist narrative, is irrespective of the Ukrainian historical experience and is a dangerous abuse of Great Patriotic War imagery in the present. Considering the legacy of the former imperial relationship between Russia and Ukraine, and the Russian state’s current interest in restoring bygone prestige, this dimension of the current threat to Ukrainian sovereignty should not be ignored.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".