Bibliographic record
Abstract
Many words have been used to name and describe the Great Ukrainian Famine of 1932-33, including “famine” and “catastrophe,” “the Holodomor,” and now “genocide.” Was the famine genocide? Was the famine part of a genocide? Is the word genocide an exaggeration? Is naming the famine a genocide part of an attempt to dramatize events for political purposes today? Is the refusal to call the famine a genocide an act of genocide denial? This article argues that, though more than seven decades have passed and the Soviet Union has come and gone, questions about genocide in Ukraine remain intertwined in the discourses and narratives surrounding conflicts over Ukraine’s economic, political, social, and cultural position between the European Union and the Russian Federation. Given the implications of this word—“genocide”—within the context of current conflicts over Ukrainian history and identity and even sovereignty, it is important to reflect on how this concept has been used and applied. This paper analyzes conflict in Ukraine in the 1930s using Raphaël Lemkin's definition of genocide, as opposed to the legal definition established by the UN Genocide Convention, and discusses the conceptual strengths of Lemkin's definition of genocide in terms of understanding a wide-spectrum of oppressive, repressive, and violent processes of empire-building and colonization that occurred in Ukraine, and which culminated in the Holodomor.
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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 | 0.004 | 0.010 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".