F.M. Dostoevsky’s Nationalism: History, Historiography, and Politics. An Old Controversy in a Post-2022 Context
Bibliographic record
Abstract
Abstract Dostoevsky’s nationalism has long been a sensitive and controversial topic in Western scholarship. At the core of the controversy is the problem of explaining the stark contrast between Dostoevsky’s philosophical message of universal love and the explicitly xenophobic, chauvinistic and war-glorifying statements found in many of his journalistic articles. Russia’s full-scale war in Ukraine has reignited the old controversy in ways that have brought to light the profound political implications involved in interpreting Russian history in a post-2022 context. Many Ukrainian intellectuals and public figures have come to question not only the appropriateness of Dostoevsky’s title as a “great humanist” but also the conventions of Dostoevsky’s reception in Western scholarship, which serve to maintain this image of the writer in the public mind, despite many of his unpalatable ideas. These sentiments are echoed by (as yet) a small group of Russianists in the West who argue for the need to reconsider Dostoevsky from a more critical, decolonizing perspective. This essay offers a historiographic review of the theme of Dostoevsky’s nationalism in Western and Russian scholarship over the past two decades. It also highlights the way in which Dostoevsky’s nationalist ideas have been used by Russian propagandists in popular media since Russia’s first invasion of Ukraine in 2014.
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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 | 0.005 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| 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".