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Record W4386824412 · doi:10.3138/seminar.59.3.1

Unsettling German Memory Culture: The Role of Archives in Natascha Wodin’s <i>Sie kam aus Mariupol</i>

2023· article· en· W4386824412 on OpenAlexvenueno aff
Friederike Eigler

Bibliographic record

VenueSeminar A Journal of Germanic Studies · 2023
Typearticle
Languageen
FieldPsychology
TopicMemory, Trauma, and Commemoration
Canadian institutionsnot available
Fundersnot available
KeywordsGermanUkrainianForgettingNazismCollective memoryNarrativeThe HolocaustNazi GermanyHistoryWorld War IISociologyGender studiesPolitical scienceLiteratureLawArtPsychology

Abstract

fetched live from OpenAlex

In Sie kam aus Mariupol (2017), Natascha Wodin reconstructs her Ukrainian family history with special focus on her mother’s experience as a forced labourer in Nazi Germany and the debilitating effects of continued discrimination in postwar Germany. Wodin’s powerful narrative draws attention to a traumatized woman whose short life coincided with violent upheavals in twentieth-century Ukrainian, Russian, and German history. Based on approaches in memory studies and the archival turn, this article argues that several archives play a central role in this autofictional text: an urban archive of Mariupol that both documents and counteracts the repeated destruction of the city over the course of the twentieth and twenty-first centuries, a collection of familial documents from Ukraine and Russia located primarily through online searches, and an archive that details the role of forced labour in Germany during the Second World War. The archive on forced labour challenges us to contemplate the kind of forgetting that has accompanied the public commemoration of the victims of Nazi Germany. Overall, Sie kam aus Mariupol exemplifies the role of literature, in particular autofictional texts of the postgeneration, in intervening in discourses on collective memory.

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)0.000
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 score0.184
Threshold uncertainty score0.589

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.033
GPT teacher head0.346
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
Published2023
Admission routes1
Has abstractyes

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