Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".