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Record W4306809702 · doi:10.1177/08883254211020819

Are Your Hands Covered in Jewish Blood? Jewish Red Army Soldiers Encountering the Aftermath of the Holocaust in the Soviet Union

2022· article· en· W4306809702 on OpenAlexaff
Anna Shternshis

Bibliographic record

VenueEast European Politics and Societies and Cultures · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicGerman History and Society
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsThe HolocaustJudaismMemoirFraming (construction)LawUkrainianSoviet unionDutyWorld War IIHistorySociologyPolitical scienceAncient historyGender studiesPoliticsArchaeology

Abstract

fetched live from OpenAlex

Soldiers and officers of the Red Army were among the first military personnel to encounter the destruction of the Ukrainian and Belarussian Jewish communities late in World War II. A significant proportion of the hundreds of thousands of Jews who served in the Red Army between 1943 and 1945 learned of the deaths of their own family members while they were in active duty. By examining the historical details and literary conventions of a small number of autobiographies and oral history interviews, the chapter discusses the range of reactions of these combatants to the destruction of their communities, from immediate retaliation to working with Soviet authorities to identify and convict collaborators. In addition, the chapter examines how a narrator’s current country of residence appears to influence the framing of his memoir.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.010
Scholarly communication0.0050.002
Open science0.0000.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.023
GPT teacher head0.206
Teacher spread0.184 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations1
Published2022
Admission routes1
Has abstractyes

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