The “Plague of Cyprian”: A revised view of the origin and spread of a 3rd-c. CE pandemic
Bibliographic record
Abstract
Abstract Kyle Harper's article on the “Plague of Cyprian” that appeared in this journal in 2015 constitutes the only comprehensive study to date of this important disease outbreak in the third quarter of the 3rd c. CE. The current article revisits the main evidence for this epidemic and corrects and improves our understanding of its origin, timeline, and spread. It contends that the disease entered the Roman Empire via Gothic invasions on the Danube rather than traveling up the Nile from inner Africa. It further argues that the disease reached the Roman Empire only after the death of Decius and cannot be connected with the latter's edict commanding sacrifices to the Roman gods, issued in 249 CE. While the pestilence indubitably exacerbated the political and military crisis of the third quarter of the 3rd c. CE, it should probably not be considered as the root of the crisis itself, as Harper has suggested.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".