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Record W3088041473 · doi:10.33137/q.i..v38i2.32234

“Forgers of Falsehood, Physicians of Nought”: Retailing Fictions in Boccaccio’s <i>Decameron</i>

2019· article· en· W3088041473 on OpenAlexvenueno aff
Tobias Foster Gittes

Bibliographic record

VenueQuaderni d italianistica · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicRenaissance and Early Modern Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPlague (disease)PoetryPortraitArtHistoryMedical professionLiteratureMedicineAncient historyArt historyFamily medicine

Abstract

fetched live from OpenAlex

Whereas Petrarch’s portrait of his doctor in Invectives Against a Physician is deliberately caricatural and seized at a glance, Boccaccio’s attitude towards doctors in the Decameron is far harder to grasp and easily overlooked. Yet, doctors and medical science are a central concern of the Decameron, whose first significant action (the brigata’s movement from the plague-afflicted city to the countryside) and activity (storytelling) are predicated on the Florentine doctors’ failure to find a remedy for the plague. Throughout the Decameron, the doctors’ glaring incapacity to help their patients is implicitly contrasted with the poets’ success in offering some measure of solace—if not a definitive cure—to those afflicted by the plague. The conventional view that poets retail fictions, and doctors, real cures, is repeatedly cast into doubt as Boccaccio reveals that all too often the real difference between doctors and poets is that doctors hawk medical fictions (their arsenal of exotic powders and decoctions) as true cures, whereas poets cloak true cures in poetic fictions. Medical fictions sicken the healthy and kill the sick; poetic fictions quicken the spirit and promote life. This counterpoising of doctors and poets (or painters), medicine and fiction in the Decameron both anticipates and contributes to Boccaccio’s lifelong defense of poetry that culminates in the 14th and 15th books of the Genealogy of the Pagan Gods.

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 score0.853
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.028
GPT teacher head0.239
Teacher spread0.211 · 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
Published2019
Admission routes1
Has abstractyes

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