Towards a Decentred History: The Study of the Holodomor and Ukrainian Historiography
Bibliographic record
Abstract
This article reviews research on the Holodomor by historians of Ukraine since the late 1980s. It examines the dominant trends in historiography, the major findings, and the current state of the field. The field itself, it argues, has grown considerably and there now exists a critical body of scholarship on the subject. For the past two decades, this scholarship has largely been dominated by the debate about whether the Holodomor constitutes genocide. Much of the focus has been on illuminating the policies, methods, and intentions of the Soviet leadership and there have been notable advances in these areas of research. Social history on this topic, on the other hand, remains largely underdeveloped. Some historians of Ukraine have begun to study the Holodomor “from below” and to explore the larger social and cultural consequences of de-kulakization, collectivization, and the Terror-Famine. This approach is crucial, the author suggests, to understanding the exceptional nature of the era. In terms of patterns of migration, family structure, religious practices, social identity, status and ranking, and attitudes towards power, authority, and political participation, the Holodomor Era fundamentally changed the way Ukrainians lived. In this respect, it represents a turning point, as momentous as perhaps any in Ukrainian history.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| 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".