Bibliographic record
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 the historiography of epidemics emerged: the natural history of diseases which was developed by microbiologists. At the same time, medical history was reborn, and its 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 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.
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 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".