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Record W2040910287 · doi:10.7202/1009271ar

Échange interculturel et transfert de représentations

2012· article· fr· W2040910287 on OpenAlexvenueno aff
Robin Beuchat

Bibliographic record

VenueArborescences Revue d études françaises · 2012
Typearticle
Languagefr
FieldArts and Humanities
TopicHistorical Art and Culture Studies
Canadian institutionsnot available
Fundersnot available
KeywordsArtHumanitiesPortraitThe RenaissanceArt history

Abstract

fetched live from OpenAlex

Parmi son imposante collection, l’humaniste italien Paolo Giovio possédait plusieurs portraits de sultans ottomans qui, comme il l’explique dans une scène célèbre de ses Elogia virorum bellica virtute illustrium (1551), avaient été copiés d’après les miniatures offertes par Barberousse à Virginio Orsini, deux capitaines que l’alliance franco-turque avait réunis sous la même bannière. Au-delà de sa valeur documentaire, cette scène nous permet d’étudier un échange interculturel unique – puisque, comme on a eu tendance à l’oublier, Orsini ne se contente pas de recevoir des présents, il en offre aussi. Examinant dans un premier temps le regard de Giovio, je montre que s’il témoigne de préjugés hostiles aux Turcs, ces préjugés relèvent avant tout d’un patriotisme italien. Ses portraits, auxquels je m’attache plus spécifiquement dans un second temps, attestent le même préjugé. Car bien qu’il revendique une esthétique de la transparence, Giovio insiste sur la nécessité d’adapter ses modèles au bon goût italien – autrement dit sur la nécessité d’une « traduction culturelle ». En étudiant le regard que Giovio porte sur l’art oriental et la manière dont il se l’approprie, cet article souhaite ainsi apporter une contribution à une histoire interculturelle de l’art et, plus généralement, à l’histoire des perceptions interculturelles à la Renaissance.

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)0.003
metaresearch head score (Gemma)0.006
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: none
Teacher disagreement score0.017
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0060.027
Scholarly communication0.0170.012
Open science0.0010.009
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0130.002

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 head0.042
GPT teacher head0.291
Teacher spread0.249 · 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
Published2012
Admission routes1
Has abstractyes

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