Bibliographic record
Abstract
Abstract The wealth of source material produced by Roman notaries has long been neglected. Only in recent decades has it been systematically evaluated by researchers. Given the fragmentary source situation in general, these atti notarili add considerable detail to our knowledge of everyday life. They provide insights not only into the Rome of the popes, which has received considerable attention, but above all into the Rome of the Roman people, much less studied. This paper focuses not on another notary, but on the question of the potential contribution of this genre of sources to the history of late medieval Rome. We see how clients belonging to different social groups approach different notaries. We understand the typical concerns and problems of Roman society, ranging from the debtor imprisoned in a dungeon to the Colonna family in their palazzo. We find confident Florentines with their business dealings, German bakers and their partnership contracts, impoverished monasteries with their growing debts. We see the building boom of the Renaissance and how Piazza Navona is transformed from a campus into a platea, as well as rural Rome within the walls and its countless vineyards.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".