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Record W2754667124 · doi:10.3138/seminar.53.3.02

Women in the Archive: Locating the International Tracing Service in German Memory Work

2017· article· en· W2754667124 on OpenAlexvenueno aff
Silke von der Emde

Bibliographic record

VenueSeminar A Journal of Germanic Studies · 2017
Typearticle
Languageen
FieldPsychology
TopicMemory, Trauma, and Commemoration
Canadian institutionsnot available
Fundersnot available
KeywordsPersecutionNazismGermanInscribed figureThe HolocaustService (business)TracingSet (abstract data type)Nazi GermanyHistoryInternet privacyComputer securityPolitical scienceLawComputer sciencePoliticsBusinessArchaeology

Abstract

fetched live from OpenAlex

The International Tracing Service in Bad Arolsen is one of the largest collections of documents related to the Holocaust and Nazi persecution in the world. Originally set up as a tracing service to help provide information for survivors and family members of victims, it has become an invaluable centre for documenting Nazi persecution since it was opened to researchers in 2007. This paper analyzes the interaction of the people of Bad Arolsen with the ITS, which was set up in their small town without their consent. By focusing on the gender relations inscribed into the archive and on the way the people of Bad Arolsen interacted with the archive, I show how we arrive at an expanded notion of the archive on the one hand and at a more multi-dimensional understanding of cultural memory on the other.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.022
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0100.013
Scholarly communication0.0110.006
Open science0.0010.007
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.067
GPT teacher head0.377
Teacher spread0.310 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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