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Record W4388439310 · doi:10.7202/1106039ar

Une brève histoire des assurances auMoyen Âge

2023· article· fr· W4388439310 on OpenAlexvenueno aff
Martin I. Boyer

Bibliographic record

VenueAssurances et gestion des risques · 2023
Typearticle
Languagefr
FieldArts and Humanities
TopicHistorical Studies and Socio-cultural Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceArtPhilosophy

Abstract

fetched live from OpenAlex

Afin de comprendre comment les contrats d’assurance se sont développés, il faut analyser les moyens qui ont été utilisés au travers des âges par les marchands et les armateurs pour réduire leurs pertes en cas d’accident ou de sinistre. Il faut savoir qu’au Moyen Âge, l’Église luttait férocement contre certaines pratiques commerciales qu’elle qualifiait d’usure. Dans ce document, je tenterai de démontrer que l’assurance maritime s’est développée beaucoup plus rapidement que l’assurance terrestre pour trois raisons principales. Premièrement, les voyages maritimes étaient plus sujets à la piraterie que ne l’étaient les voyages terrestres au brigandage; deuxièmement, les cataclysmes naturels affectent plus le commerce par mer que le commerce par terre, et troisièmement, le commerce maritime rapporte plus que le commerce terrestre.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.540
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0090.003
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.045
GPT teacher head0.264
Teacher spread0.220 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2023
Admission routes1
Has abstractyes

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