Bibliographic record
Abstract
Although the character of the disease has stirred much controversy and continues to be the subject of scientific and historical investigations, the consequences of the Black Death and plagues—demographic, economic, social, and cultural—have embroiled historians in debate for a century or more. Some historians have seen the Black Death as a sharp turning point, accounting for many subsequent events and trends in Western civilization, even those that occurred many years afterward, such as the Reformation. Others have downplayed the Black Death’s effects, seeing them at best as only accelerating trends already well in motion, originating with developments such as urbanization that reach back to the 13th century. Nonetheless, the question of the Black Death’s impact on history has concerned historians across a broad range of disciplines—demography, economics, religious studies, and psychology—employing different methods and sources. These are some of the subjects covered in this article alongside others, including the Black Death’s impact on popular revolt, women, literature, and art. The epidemiological character of the Black Death and its medical history are covered in the Oxford Bibliographies in Renaissance and Reformation article “Black Death and Plague: The Disease and Medical Thought.”
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".