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"War Culture" in German Postcards of 1914-1918

2022· article· ru· W4293222355 on OpenAlexaboutno aff
Г.Н. Канинская

Bibliographic record

VenueДиалог со временем · 2022
Typearticle
Languageru
FieldSocial Sciences
TopicHistorical Influence and Diplomacy
Canadian institutionsnot available
Fundersnot available
KeywordsGermanCensorshipIdeologyAdversaryQuarter (Canadian coin)Period (music)State (computer science)First world warWorld War IIHistoryMedia studiesArt historyLawSociologyClassicsPolitical scienceArtPoliticsAncient historyAestheticsArchaeology

Abstract

fetched live from OpenAlex

В статье рассматривается монография доктора исторических наук А.С. Медякова, изданная в 2021 г. В ней автор, на основе анализа солидного массива немецких открыток периода Первой мировой войны, показал, как формировалась «культура войны» в визуальной форме, как конструировался, поддерживался и эволюционировал в немецком обществе образ врага и союзника. Военный дискурс в книге представлен по многим срезам: социокультурному, историко-генетическому, идейно-пропаган-дистскому, сравнительному, лингвистическому. The article discusses the monograph of Doctor of Historical Sciences Alexander S. Medyakov, published in 2021. The author, who devoted a quarter of a century to collecting old postcards, for the first time in Russian historical science, showed based on the analysis of a solid array of German postcards from the period of the First World War, how the “culture of war” was formed » in visual form, how the image of the enemy and ally was designed, maintained and evolved in German society. The military discourse in the book is presented in many sections: socio-cultural, historical-genetic, ideological-propaganda, comparative, linguistic. The practice of distribution of printed materials is disclosed in detail, much attention is paid to the state and private press, competition in the postcard market, and censorship.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.767
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.022
GPT teacher head0.349
Teacher spread0.327 · 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.

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

Citations0
Published2022
Admission routes1
Has abstractyes

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