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Record W4389347945 · doi:10.13035/h.2023.11.02.55

Review of Frederick A. De Armas, «Cervantes’ Architectures: The Dangers Outside», Toronto, Toronto University Press, 2022, 384 pp. ISBN: 978-1-4875-4241-2

2023· article· es· W4389347945 on OpenAlexaboutno aff
Maxim Rigaux

Bibliographic record

VenueHipogrifo Revista de literatura y cultural del Siglo de Oro · 2023
Typearticle
Languagees
FieldArts and Humanities
TopicHistorical Art and Architecture Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedia studiesHumanitiesSociologyArt

Abstract

fetched live from OpenAlex

For his latest book, De Armas turns to the buildings in Cervantes' prose fiction and takes us on an engaging journey from La Galatea (1585) to the Persiles (1617) in nine chapters.Readers often take for granted references to or descriptions of buildings, but De Armas shows us why it is important not to do so, and how, in the case of Cervantes, they can convey new and provocative insights into some of his most studied works.In line with the «spatial turn» in literary studies, De Armas focuses on how place or space -a distinction further explained through the Chinese architect Yi-Fu Tuan's definitions of place as security and space as freedom-give meaning to the text, even when seemingly no interest is taken in the spatiality of a certain episode.Probably the most revealing chapters of the book are the ones dedicated to the elliptical and ellipsis in Cervantes' posthumous novel (chapters 7 to 9).By pointing out the lack of windows in the North in the first two books or the double focus in the last two (when often unexpected architectures are used in order to say something about the ones that are left unmentioned), De Armas convincingly suggests new readings of not only key episodes but also the broader meaning of the novel itself.

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)0.002
metaresearch head score (Gemma)0.004
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.041
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0060.011
Science and technology studies0.0010.002
Scholarly communication0.0040.005
Open science0.0020.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0170.015

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 head0.015
GPT teacher head0.242
Teacher spread0.227 · 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
Published2023
Admission routes1
Has abstractyes

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