The Causes of Ukrainian Famine Mortality, 1932-33
Bibliographic record
Abstract
We construct large, unique panel data to study the causes of Ukrainian famine mortality (Holodomor) during 1932-33 and document several new facts: i) Ukraine (the Soviet Union) produced enough food in 1932 to avoid famine in Ukraine (the Soviet Union); ii) mortality was increasing in the pre-famine ethnic Ukrainian population share and unrelated to food productivity across regions; iii) this pattern exists across the Soviet Union, even outside of Ukraine; iv) the pattern was similar at different administrative levels; v) migration restrictions exacerbated mortality; vi) actual and planned grain procurement were increasing, while actual and planned grain retention (production minus procurement) were decreasing in the ethnic Ukrainian population share across regions.Anti-Ukrainian bias in Soviet policy explains up to 92% of famine mortality in Ukraine and 77% in Ukraine, Russia and Belarus; approximately half of the total effect comes from bias in the centrally planned food procurement policy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".