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Record W2288899833 · doi:10.4000/mefra.3250

L’acqua come elemento di lusso nella cultura romana : da Varrone alla Historia Augusta

2016· article· fr· W2288899833 on OpenAlexaff
Christer Bruun

Bibliographic record

VenueMélanges de l École française de Rome Antiquité · 2016
Typearticle
Languagefr
FieldSocial Sciences
TopicClassical Antiquity Studies
Canadian institutionsChristie (Canada)
Fundersnot available
KeywordsArtHumanities

Abstract

fetched live from OpenAlex

Durante il periodo repubblicano l’elite romana era sempre più propensa a spendere risorse per scopi stravaganti. La legislazione contro il lusso tenne passo con questa tendenza, in particolare dal II secolo a.C. in poi. Le leges sumptuariae prendevano di mira abiti e gioielleria femminile, banchetti e funerali. Critica da posizioni moralistiche fu rivolta anche alle costruzioni private fuori della norma. Nelle leggi non si fa però mai menzione di aqua, anche se enormi quantità ne furono condotte agli abitanti di Roma e in altri luoghi. Questo contributo analizza il ruolo dell’acqua nel contesto della luxuria romana. Un primo segno di critica contro l’uso eccessivo d’acqua s’incontra in un testo di Varrone. Ciononostante l’uso dell’acqua non divenne mai parte del discorso sul luxus, come lo furono invece i banchetti e i palazzi lussuosi, perché i sistemi di rifornimento urbano non erano ancora sufficientemente sviluppati durante la tarda Repubblica, quando gli elementi del discorso sul luxus furono stabiliti. Più tardi, durante il Principato, i potenti Romani che usarono ingenti quantità d’acqua nei loro horti usufruivano di un permesso dell’imperatore. Era quindi impossibile criticare la loro prassi. Le thermae offrivano l’unico spunto per una critica, perché il concetto moralistico di mollitia poteva essere applicato alla cultura balneare.

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)0.003
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
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.014
Scholarly communication0.0090.003
Open science0.0010.003
Research integrity0.0010.005
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 head0.026
GPT teacher head0.285
Teacher spread0.258 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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