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Record W2874448 · doi:10.1057/9780230522619_8

Corruptible Bodies and Contaminating Technologies: Jesuit Devotional Print and the 1656 Plague in Naples

2005· book-chapter· en· W2874448 on OpenAlexaff
Rose Marie San Juan

Bibliographic record

VenuePalgrave Macmillan UK eBooks · 2005
Typebook-chapter
Languageen
FieldArts and Humanities
TopicHistorical and Religious Studies of Rome
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsPortraitSAINTArtArt historyIconPatron saintHistoryComputer science

Abstract

fetched live from OpenAlex

Few who contracted the bubonic epidemic in Naples in 1656 lived to tell the tale, but some that did attributed their recovery to an unprepossessing printed portrait of Jesuit saint Francis Xavier (Figure 1). For these survivors, the power of this portrait to arrest the flow of contagion was envisaged as bodies pressed and impressed on each other, conjoined in the act of healing much as they were believed to be in the transmission of deadly disease. Giovanni Battista de Angelis, for instance, testified that he, like many of his neighbours, bought a printed portrait of this saint from a street seller, and always carried it inside his shirt except at night when he placed it under his pillow.1 One day he found a huge ulcer in the area of his heart, and after seeing death approach him, he took the portrait of the saint and placed it on the affected area. Immediately he fell asleep and awoke one hour later to find the print and his shirt full of blood and festering while the ulcer had disappeared. The portrait became, like a relic or an icon, the carrier of the presence of the saint,2 but this presence was short-lived and not contained within the materiality of the print, which typically is ignored and even discarded after it has healed the body, usually by extracting corrupt bodily fluids. In fact, Giovanni de Angelis quickly turns his attention from the image of the saint to his own body, as he feels the corrupt liquids trapped inside, measures these in relation to visible body parts, and sees them outside of himself, expelled from his interior and subsumed into the printed body. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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)0.002
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: none
Teacher disagreement score0.065
Threshold uncertainty score0.129

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0240.025
Scholarly communication0.0090.004
Open science0.0010.007
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0070.001

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 head0.019
GPT teacher head0.201
Teacher spread0.182 · 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
Published2005
Admission routes1
Has abstractyes

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