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Record W2491348067 · doi:10.1093/fh/crv021

Money and Political Economy in the Enlightenment

2015· article· en· W2491348067 on OpenAlexaff
Richard A. Kleer

Bibliographic record

VenueFrench History · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHistorical Economic and Social Studies
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsEnlightenmentPoliticsEconomic historyFoundation (evidence)Political economyHistoryPolitical scienceClassicsSociologyLawPhilosophyTheology

Abstract

fetched live from OpenAlex

The book is classified as belonging to the fields of economic history and the history of ideas. This is wholly accurate and represents one of the book’s chief strengths; in most essays, there is a tight connection between Enlightenment ideas and the historical context in which they were originally offered. Contemporary economic institutions, when relevant to the exegesis, are described with loving attention in a manner accessible to non-specialists. Every author offers numerous and often lengthy quotations from the original works under consideration. Almost without exception, the scholarship is of high quality. The book has a very loose unity in that 4.5 of the 7 chapters (not counting the obligatory introductory essay) take money for their principal theme. And all fit comfortably inside the category, broader still, of political economy. But the collection will find an audience on account of its parts, not the whole. Most readers of this review will probably have experienced the considerable pleasure of reading widely in the literature of early modern political economy. The real work arrives when it comes time to collect one’s thoughts and write something meaningful about the experience. I have identified five basic strategies in the essays collected here.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.020
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.005
Scholarly communication0.0070.004
Open science0.0000.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0200.002

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.

Opus teacher head0.061
GPT teacher head0.202
Teacher spread0.141 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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
Published2015
Admission routes1
Has abstractyes

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