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Record W2031681750 · doi:10.1179/019713606806082201

The Technical Examination of a Painting that Passed Through the Hands of Sienese Restorer and Forger Icilio Federico Joni

2006· article· en· W2031681750 on OpenAlexaff
Kim Muir, Narayan Khandekar

Bibliographic record

VenueJournal of the American Institute for Conservation · 2006
Typearticle
Languageen
FieldArts and Humanities
TopicConservation Techniques and Studies
Canadian institutionsQueen's University
FundersHarvard University
KeywordsPaintingContext (archaeology)ArtVisual artsArt historyOrder (exchange)HistoryBusinessArchaeology

Abstract

fetched live from OpenAlex

A panel painting of the Crucifixion belonging to the Fogg Art Museum and attributed to an imitator of the 14th-century northern Italian painter Altichiero underwent technical examination to address questions about its authenticity. Suspicions about the painting had been raised on technical grounds and because it was purchased from the famous Sienese restorer and forger Icilio Federico Joni (1866–1946). The materials and technique of the painting were studied and compared to 14th-century Italian panel painting practices and to restoration and forging techniques described by Joni. The examination shows that the distinction between a heavily restored painting and a fake can be ambiguous, particularly when only a vestige of the original remains. The issues raised when dealing with such “renovated ruins,” as well as the social context that created an environment conducive to the production and marketing of fakes in Italy in the late 19th and early 20th centuries, are also discussed.

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)0.000
metaresearch head score (Gemma)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0030.002
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.041
GPT teacher head0.259
Teacher spread0.218 · 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
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
Published2006
Admission routes1
Has abstractyes

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