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Record W4386469411 · doi:10.5195/ahea.2023.484

Narrating the Danube Swabian Identity and Experience from Women's Perspective

2023· article· en· W4386469411 on OpenAlexaff
Agatha Schwartz

Bibliographic record

VenueHungarian Cultural Studies · 2023
Typearticle
Languageen
FieldPsychology
TopicMemory, Trauma, and Commemoration
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsNarrativeDiasporaMemoirGender studiesHistoryHomelandPerspective (graphical)Identity (music)GenocideFamineSociologyPolitical scienceLiteratureAestheticsArtLawArt historyArchaeologyPoliticsVisual arts

Abstract

fetched live from OpenAlex

This article uses selected memoirs by American women who came from the Danube Swabian minority in present-day Hungary and Serbia (former Yugoslavia). The entire ethnic group was expelled from the region at the end of World War II. All five memoirs were published in the new millennium. This article examines how the narratives frame memories of a prewar happy childhood from young women’s perspective. The childhood memories are presented in stark contrast to the authors’ postwar experiences of expulsion, sexual violence, genocide, flight, and the eventual building of a new life in a new country. All narratives document the brutality with which the Danube Swabian communities were destroyed, particularly in Yugoslavia. Nostalgic overtones about a lost homeland intersect with a lasting feeling of being atopos—i.e., “of no place,” in exile and in the diaspora. While most of the narratives emphasize Danube Swabian victimhood, one narrative stands out in its attempt to create a more multidirectional approach to memory about World War II. agathas@uottawa.ca

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.024
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0150.014
Scholarly communication0.0060.003
Open science0.0010.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.120
GPT teacher head0.397
Teacher spread0.277 · 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 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

Citations1
Published2023
Admission routes1
Has abstractyes

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