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Record W3157927667 · doi:10.21226/ewjus646

Famine As an Instrument of Nazi Occupation Policy in Ukraine, 1941-44

2021· article· en· W3157927667 on OpenAlexvenueno aff
O. Lysenko, T. Zabolotna, O. Maievskyi, Mark B. Baker

Bibliographic record

VenueEast/West Journal of Ukrainian Studies · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicEastern European Communism and Reforms
Canadian institutionsnot available
Fundersnot available
KeywordsFamineDeportationIndigenousPopulationNazismWorld War IIPolitical scienceThe HolocaustNazi GermanyGeographyGermanEconomic growthEconomyPolitical economyDevelopment economicsSociologyLawImmigrationEconomicsArchaeology

Abstract

fetched live from OpenAlex

The article examines a range of questions tied to Nazi Germany’s socio-economic policies in occupied Ukraine during World War II. In line with implementing the General Plan “Ost,” the top leadership of the Third Reich intended to cleanse the “eastern territories” of its “superfluous” population for German colonization. As contained in the “Principles of Economic Policy in the East,” these directives provided for the physical extermination of tens of millions of people in various ways, as well as the deportation of part of the indigenous population to remote areas. Ukraine’s economic exploitation was built in such a way that it doomed the local urban and rural societies to a miserable, half-starved existence. The systematic seizure of food for the needs of the Wehrmacht, the Reich, and its allies made the death of the inhabitants of the occupied lands only a matter of time. The instrumentalization of terror by famine was manifested, on the one hand, by the creation of special structures that requisitioned food resources, and on the other by establishing norms of food supplies that were below the minimal needs for existence. As well, the strict regulation of trade set limits to the sources of food products that could be brought to the cities. This caused mass starvation. The deaths and the diseases that followed created hundreds of thousands of victims among Ukrainians.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.273
Threshold uncertainty score0.481

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.068
GPT teacher head0.380
Teacher spread0.312 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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