How the Holodomor Can Be Integrated into our Understanding of Genocide
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 <em>Harvest of Sorrow</em> 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it