Regional variations of 1932–34 famine losses in Ukraine
Bibliographic record
Abstract
AbstractYearly estimates of urban and rural direct losses (excess deaths) from the 1932–34 famine are presented for the oblasts of Soviet Ukraine. Contrary to expectations, the highest losses are not found in the grain-producing southern oblasts, but in the north-central Kyiv and Kharkiv oblasts. Several hypotheses are proposed and tested to explain this finding. No single hypothesis provides a comprehensive explanation. Losses in some oblasts are due to specific factors, while losses in other oblasts seem to be explained by a combination of economic and political factors. Quantitative analyses are presented of resistance and Soviet repressions in 1932, and effects of the food assistance program and historical-political factors on direct losses in 1933 are analyzed.Des estimations annuelles de pertes (décès excédentaires) directement attribuables à la famine de 1932-34 sont présentées pour les zones urbaines et rurales d’Ukraine sovietique. Contrairement aux attentes, les pertes les plus importantes n’étaient pas dans la région méridionale productrice de grain, mais plutôt dans la région du nord-centre, soit Kiev et Kharkiv. Plusieurs hypothèses sont proposées et mises à l’épreuve pour vérifier cette conclusion. Cependant, aucune hypothèse, à elle seule, ne fournit une explication complète. Dans certaines régions, les pertes sont causées par des facteurs précis, alors que dans d’autres, les pertes sont expliquées par une combinaison de facteurs économiques et politiques. Des analyses quantitatives sont présentées sur la résistance et les répressions sovietiques en 1932. L’effet du programme d’assistance alimentaire et les facteurs politico-historiques attribuables directement aux pertes en 1933 est également analysé.
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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 | 0.000 | 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 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".