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Record W2808231890

Review of Riccardo Saccenti’s Debating Medieval Natural Law: A Survey (Notre Dame, IN: University of Notre Dame Press, 2016)

2018· article· en· W2808231890 on OpenAlexaff
Peter Karl Koritansky

Bibliographic record

VenueAnalecta hermeneutica · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicSeventeenth-Century Political and Philosophical Thought
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsNatural lawNatural (archaeology)Middle AgesIdeologyReading (process)PhilosophyPeriod (music)LawTransition (genetics)Canon lawClassicsEpistemologyHistoryHumanitiesPolitical sciencePoliticsTheologyAestheticsArchaeology
DOInot available

Abstract

fetched live from OpenAlex

In this short monograph, Riccardo Saccenti surveys the various and competing interpretations of natural law and natural right from the late Middle Ages through the modern period. As “a survey,” the intention of this book is not so much to advance and defend a central thesis about natural law, but rather to paint a picture of how the various interpreters of natural law have responded to the most important primary texts (of Gratian, Thomas Aquinas, Duns Scotus, William of Ockham, and John Locke, for example) and to one another. One of the issues with which Saccenti is most concerned is the transition from the medieval understanding of natural law inspired by an Aristotelian concept of nature to a modern theory of natural individual rights. Where exactly does this transition begin and where is it more or less consummated? A reader of this book will be impressed by the complexity of this debate (among others) and the nuances of difference between its participants. Saccenti’s account of it succeeds in exposing the folly of overly simplistic readings of intellectual history that are ideologically motivated, and reminds us what a careful reading of primary and secondary sources can achieve.

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)0.000
metaresearch head score (Gemma)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.883
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.048
GPT teacher head0.265
Teacher spread0.217 · 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 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
Published2018
Admission routes1
Has abstractyes

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