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Record W3112207128 · doi:10.1002/bewi.202000013

Baroque Science, Experimental Art? Jusepe de Ribera and other Neapolitan Sceptics

2020· article· en· W3112207128 on OpenAlexaff
Itay Sapir

Bibliographic record

VenueBerichte zur Wissenschaftsgeschichte · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicVisual Culture and Art Theory
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsBaroquePassionsTheme (computing)Context (archaeology)SkepticismPaintingFaithNarrativeArtPhilosophyArt historyLiteratureEpistemologyHistoryComputer science

Abstract

fetched live from OpenAlex

Current attempts by historians of science to revise the narrative of the Scientific Revolution by using the concept of the Baroque have important implications for art history. Correspondences between baroque art and baroque science gain new complexity when the rational, epistemologically optimistic image of the New Science is put in doubt. Rather than a method of objective observation, early seventeenth-century science and art share an acceptance of the constructed nature of reality, of human epistemological limitations and of the role of passions in the observation of the world. While Caravaggio has revolutionised art precisely through his interest in questions of knowledge and sensorial perception and by his subversive transformation of Renaissance epistemological values and ideals, this article concentrates on the work of Jusepe de Ribera, who made the senses and their shortcomings a major theme of his pictorial research. Ribera's epistemology is examined in the context of contemporary Neapolitan philosophy and science. Through the confrontation of some of the Spagnoletto's paintings with the work of figures such as Giovanni Battista della Porta, Federico Cesi and particularly Colantonio Stigliola, it becomes clear that early modern Neapolitan faith in rational knowledge was more ambiguous than is sometimes assumed.

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 categoriesMeta-epidemiology (narrow), Insufficient 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: Empirical
Teacher disagreement score0.919
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.039
GPT teacher head0.273
Teacher spread0.234 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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