Формирование В Советской Историографии 30-Х – Начала 90-Х Годов Хх Века Представлений О Поздней Античности Как Особом Историческом Периоде
Bibliographic record
Abstract
В статье, на основе изучения публикаций советского периода, посвященных истории Римской империи и постримских государств в IV–VII вв., показано, какие представления о поздней античности, как особом историческом периоде, формировались в советской историографии с середины 1930-х до начала 1990-х гг. Современные ученые рассмотрение поздней античности как особого периода ведут от английских историков третьей четверти XX в. А. Джонса и П. Брауна. Автор приходит к выводу, что независимо от работ этих ученых в советской исторической науке формировалось собственное своеобразное представление об особом позднеантичном периоде римского и постримского мира. The article, based on the study of the publications of the Soviet period on the history of the Roman Empire and post-Roman states in the 4th–7th centuries, shows which ideas about late antiquity, as a special historical period, were formed in the Soviet historiography in 1930s – early 1990s. Modern scientists consider late antiquity as a special period relying on works by A. Jones and P. Brown, English historians of the third quarter of the 20th century. The author comes to the conclusion that irrespective of the works of these scientists Soviet historical science formed its own unique idea of this special Roman and post-Roman late antiquity period.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.011 |
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".