Bibliographic record
Abstract
Russian Abstract: Проблематика эпидемий все больше привлекает внимание ученых в последние годы. Между тем у истории эпидемий существует собственная историография, которая восходит к творчеству врача Гиппократа и историка Фукидида, описавшего «афинскую чуму». До XIX в. историки следовали в фарватере их идей, но вследствие быстрого прогресса медицинского знания почти утратили интерес к проблематике эпидемий. В начале ХХ в. вследствие бурного развития микробиологии и эпидемиологии возникла новая форма историографии эпидемий – естественнонаучная история болезни, которую стали развивать врачи=микробиологи, сделавшие предметом своего изучения историю контактов человечества с микробами, а также деятельность «охотников за микробами». Тогда же новое рождение пережила и медицинская история, представители которой стремились доказать врачам полезность изучения древних медицинских текстов. Среди представителей нового поколения медицинских историков со временем появились авторы, внесшие вклад в развитие историографии эпидемий. К концу ХХ в. в их числе оказалось много врачей=энтузиастов, не связанных профессионально ни с микробиологическими научными центрами, ни с центрами изучения медицинской истории. С 1970=х годов под влиянием ряда факторов к изучению эпидемической проблематики обращается все больше профессиональных историков, для которых история эпидемий – это неотъемлемая часть истории общества, его социальной, культурной и политической жизни. В последние четверть века наблюдается также и развитие популярной историографии эпидемий, которая стала возможна благодаря активизации различных исследователей=гуманитариев и журналистов, пытающихся придать истории эпидемий более живой и эмоциональный характер. Большое влияние на распространение новых подходов к изучению истории эпидемий в настоящее время оказывают СМИ, обращающие внимание общества на новых угрозах человеческой цивилизации в лице современных эпидемий – птичьего гриппа, атипичной пневмонии, коронавирусной инфекции. English Abstract: The problems of epidemics have increasingly attracted the attention of researchers in recent years. The history of epidemics has its own historiography, which dates to the physician Hippocrates and the historian Thucydides. Up to the 19th century, historians followed their ideas, but due to the progress in medical knowledge that began at that time, they almost lost interest in the problems of epidemics. In the early 20th century, due to the development of microbiology and epidemiology, a new form of historiography of epidemics emerged: the natural history of diseases which was developed by microbiologists. At the same time medical history was reborn, whose representatives saw their task as proving to physicians the usefulness of studying ancient medical texts. Among the representatives of the new generation of medical historians, authors who contributed to the development of the historiography of epidemics eventually emerged. By the end of the 20th century, they included many physician=enthusiasts. Since the 1970s, influenced by many factors, more and more professional historians, for whom the history of epidemics is an integral part of the history of society. The last quarter century has also seen a rapid growth in popular historiography of epidemics, made possible by the activation of various humanities researchers and journalists trying to make the history of epidemics more lively and emotional. A great influence on the spread of new approaches to the study of the history of epidemics is now being exerted by the media, focusing public attention on the new threats to human civilization in the form of modern epidemics.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.008 | 0.061 |
| Scholarly communication | 0.023 | 0.039 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.007 | 0.011 |
| 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".