Parties involved in ordinary violence in the Latin Quarter of Paris according to notarial acts: Victims and aggressors
Bibliographic record
Abstract
The present paper is a research on the intersection between social history and history of law. The attention is focused on fights, quarrels, and manslaughters that took place in Paris during the first part of the 16 century. The main source for this study is notarial acts, which are preserved in the Minutier Central of the French National Archives. It has been revealed that the two parties of the conflict asked for intervention of a royal notary in order to resolve their conflict or to sign the deal that was already discussed. That would permit Parisians to avoid dealing with the complicated judicial system. As a result of the analysis of 214 notarial acts, the following patterns have been discovered: firstly, the subjects of most agreements (122) were beatings and fights; secondly, the participants in such agreements were mostly small artisans and bourgeois; thirdly, most of them lived in the Latin Quarter, i.e., in the area where the offices of notaries, whose archives formed the basis of the study, were located, as well as in the surrounding suburbs; fourthly, apprentices of various professions and typographers turned out to be the aggressors in a higher number of cases, while day laborers were more often the victims. The obtained results broaden our vision about the French judicial system in the 16th century that comprised various social institutions for conflict resolution.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".