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Record W4400407526 · doi:10.1093/gerhis/ghae034

Writing and Rewriting the Reich: Women Journalists in the Nazi and Post-War Press

2024· article· en· W4400407526 on OpenAlexaboutno aff
Jennifer Lynn

Bibliographic record

VenueGerman History · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean history and politics
Canadian institutionsnot available
Fundersnot available
KeywordsNazismRewritingNazi GermanyPolitical scienceLawComputer sciencePoliticsProgramming language

Abstract

fetched live from OpenAlex

In an impressively researched and compelling read, Writing and Rewriting the Reich: Women Journalists in the Nazi and Post-War Press makes a significant contribution to the scholarship of the Third Reich and postwar West Germany. Deborah Barton’s book intersects with the historiography on gender and German history, female perpetrators in the Third Reich, and postwar memory. Her work explores women’s agency as journalists, how they navigated power in the Third Reich, their role in covering or hiding Nazi brutality and, significantly, how they used gendered assumptions of ‘apolitical’ work to rewrite their own narratives after 1945. Barton’s analysis reveals that the Nazi regime utilized women journalists in a variety of ways: as agents of ‘soft power’ which normalized everyday life in the Third Reich; as women who ‘beautified’ war and occupation; and as writers who propagated National Socialist ideology. Analysing the complex negotiations surrounding gender and power, Barton shows that even as women occupied a subordinated position in Nazi Germany, privileged women built their careers as journalists and reaped the benefits from their status in the racially defined Third Reich.

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)0.005
metaresearch head score (Gemma)0.009
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0080.013
Scholarly communication0.0120.009
Open science0.0010.004
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0150.004

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 head0.034
GPT teacher head0.297
Teacher spread0.263 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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