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Record W2765104676 · doi:10.1525/ca.2017.36.2.288

Take-Away Art: Ekphrasis and Appropriation in Martial's Apophoreta 170–82

2017· article· en· W2765104676 on OpenAlexaff
Carolyn MacDonald

Bibliographic record

VenueClassical Antiquity · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicClassical Antiquity Studies
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsAppropriationPoeticsMartial artsBanquetLiteratureCultural appropriationArtSection (typography)Capital (architecture)PoetryPower (physics)HistoryArt historyVisual artsAestheticsPhilosophy

Abstract

fetched live from OpenAlex

This paper examines the cultural antagonisms of Martial's Apophoreta 170–82, a unique series of epigrammatic gift-tags for artworks to be given away during the Saturnalia. In these poems, I argue, Martial thematizes and enacts Rome's transformative appropriation of cultural capital from Greece and elsewhere. First, he adopts the Hellenistic trope of the ekphrastic gallery tour in order to evoke the “museum spaces” of the Flavian city, where artworks became testaments to the power and culture of Rome (Section 1). While evoking these masterpiece collections, however, the epigrams in fact describe miniatures changing hands at a banquet. Martial thus tropes a second Roman practice of appropriation, namely the widespread consumption of transmedial miniature copies (Section 2). Third and finally, the epigrams dramatize the vulnerability of plundered objects by reevaluating their significance within the Roman frameworks of Latin literature and the Saturnalia (Section 3). In this miniature ekphrastic series, then, Martial's apophoretic poetics converge with Roman forms of appropriation both imperial and domestic, concrete and conceptual.

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)0.001
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: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.010
Scholarly communication0.0040.002
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.042
GPT teacher head0.339
Teacher spread0.297 · 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".

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Citations1
Published2017
Admission routes1
Has abstractyes

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