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An Ancient Scheme: The Mississippi Company, Machiavelli, and the Casa di San Giorgio (1407–1720)This article is part of a broader project. It was presented at seminars at Tulane University (2012), at McGill University (2013), at Duke University (2013), at the University of Utrecht (2014) and at the conference of the Economic History Society in a panel organized by Larry Neal (2014). I wish to thank the colleagues of these institutions for the invitations, the discussions, the criticisms and the support.

2015· book-chapter· en· W2485255968 on OpenAlexaboutno aff
Carlo Taviani

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicHistorical Economic and Social Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFifteenthThe RenaissanceGermanInstitutionLegal historyHistoryClassicsArt historyLawPolitical scienceArchaeology

Abstract

fetched live from OpenAlex

Abstract German legal historians of nineteenth and twentieth centuries defined the main characteristics of the corporations and believed that one renaissance institution, the Casa di San Giorgio at Genoa (1407–1805), was similar to the corporations of later centuries. This paper proposes to reverse this perspective: did the founders of early modern corporations know the financial model of the fifteenth century Casa di San Giorgio? The research shows the connection between the model of the Casa di San Giorgio and the Mississippi Company of John Law (1720), the famous financial scheme and bubble. The history of the Casa di San Giorgio was mainly transmitted through a passage of Machiavelli’s History of Florence (VIII, 29). The paper offers new biographical evidence that Law had been to Genoa and introduces sources connecting the genesis of Law’s scheme for the Mississippi Company in France with the model of the Casa di San Giorgio.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience 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: Empirical
Teacher disagreement score0.273
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0050.009
Scholarly communication0.0000.000
Open science0.0020.002
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 head0.068
GPT teacher head0.223
Teacher spread0.154 · 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

Citations37
Published2015
Admission routes1
Has abstractyes

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