Introduction: Thinking about medieval Europeans in their natural world
Bibliographic record
Abstract
How much were medieval Italians themselves responsible for the food shortage that by late spring 1347 was affecting about half the population of Tuscany, for the onset that summer in Sicily and Genoa of an epidemic which would in a few years kill half or more of the European population, or for the buildings smashed and hundreds of deaths in Venice and further northeast in an earthquake of January 1348? Ought those events be related to unsurpassed flooding across central Europe in July 1342, and the crash of English grain yields to 40 per cent of normal in 1348–52? Did the spread of an exotic animal, the rabbit, in thirteenth-century England and the Low Countries have anything to do with the simultaneous extirpation of native wild boar from Britain? And the arrival of an exotic fish, the common carp, in France at the very time that native salmon were vanishing from streams of coastal Normandy? Was any of this change to biodiversity connected to medieval classification of the beaver as a fish?
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".