The Causes of Ukrainian Famine Mortality, 1932–33
Bibliographic record
Abstract
Abstract We construct a novel panel dataset for interwar Soviet Union to study the causes of Ukrainian famine mortality (Holodomor) during 1932–3 and document several facts: (1) Ukraine produced enough food in 1932 to avoid famine in Ukraine; (2) 1933 mortality in the Soviet Union was increasing in the pre-famine ethnic Ukrainian population share and (3) was unrelated to food productivity across regions; (4) this pattern exists even outside of Ukraine; (5) migration restrictions exacerbated mortality; (6) actual and planned grain procurement were increasing and actual and planned grain retention (production minus procurement) were decreasing in the ethnic Ukrainian population share across regions. The results imply that anti-Ukrainian bias in Soviet policy contributed to high Ukrainian famine mortality, and that this bias systematically targeted ethnic Ukrainians across the Soviet Union.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
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 teacher head, 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".