Historians As Enablers? Historiography, Imperialism, and the Legitimization of Russian Aggression
Bibliographic record
Abstract
This essay raises the issue of historians’ responsibility to the communities that they study. While some purported version of history has been central to the Kremlin’s justifications for Russia’s aggression against Ukraine, the region’s historians have failed to make a stand against this misuse of history. Moreover, in many instances they endorsed and disseminated the Kremlin’s narratives about Ukraine’s past and present. Aiming to explain the anti-Ukrainian biases that have become well entrenched in both Western academia and Western public opinion, this essay examines the regional subfield of area studies, to which Ukrainian studies are usually relegated, as well as the expectations and agenda of the Western-educated public. I argue that the subfield is dominated by Russian studies and frequently uncritically adopts the positions, concepts, and explanations of Russia’s imperialist ideologists. At the same time, Western public opinion, while opening up to the historical injustices committed by Western empires, still sees the world through retrograde imperial lenses. The essay also discusses in detail what happens when researchers shaped by both these trends write Ukrainian history. Looking for ways forward, I suggest rethinking the issue of intellectual responsibility and “de-imperialization” of Ukraine’s Western historiography.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.034 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".