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Record W7042356161

Parties involved in ordinary violence in the Latin Quarter of Paris according to notarial acts: Victims and aggressors

2018· article· en· W7042356161 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicMedieval and Early Modern Justice
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Intervention (counseling)ApprenticeshipLatin AmericansSocial conflictOrder (exchange)Representation (politics)Social control
DOInot available

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.248
GPT teacher head0.538
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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