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Record W3089215620 · doi:10.33137/q.i..v38i1.31149

Between Reality and Symbol: Fierce Dogs and Ferocious Wolves in the <i>Decameron</i>

2018· article· en· W3089215620 on OpenAlexvenueno aff
Julia M. Cozzarelli

Bibliographic record

VenueQuaderni d italianistica · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicRenaissance and Early Modern Studies
Canadian institutionsnot available
Fundersnot available
KeywordsInterpretation (philosophy)Symbol (formal)Representation (politics)WifeHumanityComedyArtLiteratureHistoryPhilosophyTheologyLinguisticsLaw

Abstract

fetched live from OpenAlex

Non-human animals have a long history of being utilized to understand human nature, and both wild and domestic canines have been particularly intertwined with humanity since ancient times. This article examines the representation of animals, and specifically of dogs and wolves, in Boccaccio’s Decameron. The analysis posits Boccaccio’s canine portrayals as multifaceted in nature, serving as tools for the interpretation of human behavior and also reflective of shifting views on the purpose of animal portrayals in literature. The article explores these ideas, in part, through interpretation of Boccaccio’s canines in comparison to those in other sources, with an emphasis on Dante’s Divine Comedy. Given the fact that the Commedia hosts a proliferation of allegorical beasts whereas the Decameron focuses on portrayals of commonplace, living animals, one might expect significant differences in the texts’ treatment of dogs and wolves. Yet, although differences exist, a study of both texts reveals a common outlook on the canine family; and the Decameron’s treatment of dogs and wolves not only reflects that of the Commedia, but also the confluence of wolves and dogs with one another. The article’s primary subjects of discussion from the Decameron are the widely-read story of Nastagio degli Onesti’s otherworldly vision in V.8, and the lesser-known novella IX.7, featuring Talano d’Imole, his wife Margherita, and a brutal wolf attack. In addition to examining literary sources, the article touches upon hierarchical views of wild and domestic canines in daily life from the period for its analysis.

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 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 score0.647
Threshold uncertainty score0.779

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 studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.057
GPT teacher head0.275
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 teacher head, 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
Published2018
Admission routes1
Has abstractyes

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