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Magic, Memory and Natural Philosophy in the Sixteenth and Seventeenth Centuries

2015· article· en· W1517857996 on OpenAlexvenueno aff
Stephen Clucas, Hilary Gatti

Bibliographic record

VenueAestimatio Sources and Studies in the History of Science · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Philosophy and Science
Canadian institutionsnot available
Fundersnot available
KeywordsMAGIC (telescope)AlchemyNatural philosophyLiteratureNatural (archaeology)ClassicsPhilosophyHistoryArtArt historyEpistemologyArchaeology

Abstract

fetched live from OpenAlex

This collection of Stephen Clucas's articles addresses the complex interactions between religion, natural philosophy and magic in sixteenth- and seventeenth-century Europe. The essays on the Elizabethan mathematician and magus John Dee show that the angelic conversations of John Dee owed a significant debt to mediaeval magical traditions and how Dee's attempts to communicate with spirits were used to serve specific religious agendas in the mid-seventeenth century. The essays devoted to Giordano Bruno offer a reappraisal of the magical orientation of the Italian philosopher's mnemotechnical and Lullist writings of the 1580s and 90s and show his influence on early seventeenth-century English understandings of memory and intellection. Next come three studies on the atomistic or corpuscularian natural philosophy of the Northumberland and Cavendish circles, arguing that there was a distinct English corpuscularian tradition prior to the Gassendian influence in the 1640s and 50s. Finally, two essays on the seventeenth-century Intelligencer Samuel Hartlib and his correspondents shows how religion alchemy and natural philosophy interacted during the 'Puritan Revolution'.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.997
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0030.009
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.120
GPT teacher head0.279
Teacher spread0.159 · 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.

Study designTheoretical or conceptual
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

Citations1
Published2015
Admission routes1
Has abstractyes

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