How the Holodomor Can Be Integrated into our Understanding of Genocide
Bibliographic record
Abstract
The study of the Holodomor should be integrated into a broader understanding of genocide as a whole, given that a consensus that has evolved among a substantial group of scholars that the Ukrainian Famine of 1932–33 fits the general template of genocide. Raphael Lemkin, who introduced this concept into the legal structure of the international system, was clearly aware of the famine of 1932–33 and developed a notion of the “Soviet Genocide in the Ukraine” as a multi-pronged genocidal assault on the Ukrainian people. The events of the Holodomor remained largely unknown to the general Western public until the publication of Robert Conquest’s Harvest of Sorrow in 1986. Presently, the links between the study of the Holodomor and genocide studies in North America are relatively underdeveloped. As such, there are many aspects of genocide studies that could be illuminated by an understanding of the Holodomor. These include its examination as a “Communist genocide” as per Mao’s 1950s famine or Cambodia, but perhaps more specifically within the context of Stalin’s actions in the 1930s. Another important aspect is the problem of isolating ethnic from social and political categories: the Holodomor saw a concomitant attack on the Ukrainian intelligentsia and Ukrainian language and culture. The question of the numbers of victims remains controversial, although the figure of 3–5 million Ukrainians who died in Ukraine and the Kuban seems to withstand scrutiny. Finally, there is the question of intentionality. Here, in light of recent interpretations of international law, it seems quite clear that Stalin was responsible for genocide in the case of 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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.007 | 0.071 |
| Scholarly communication | 0.014 | 0.031 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.012 |
| 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".