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Record W4405493315 · doi:10.1086/732758

Shipwreck Assemblages and Network Analysis: Reconstructing the Furniture Trade in the Mediterranean Using First-Century BCE Shipwrecks

2024· article· en· W4405493315 on OpenAlexaff
Carrie E. Atkins

Bibliographic record

VenueAmerican Journal of Archaeology · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicAncient Mediterranean Archaeology and History
Canadian institutionsGeneral Electric (Canada)
Fundersnot available
KeywordsMediterranean climateGeographyArchaeologyHistory

Abstract

fetched live from OpenAlex

Elite Romans residing in opulent villas in central Italy during the first century BCE are generally viewed as the main consumers driving the trade in luxury goods. However, evidence from shipwrecks shows this is not the full picture. This article utilizes assemblage theory and network analysis to examine relationships between luxury furniture and shipwreck assemblages in the Mediterranean in the first century BCE. It starts first with discussion of couches (klinai) and tables from five shipwrecks during this period and one from the first century CE to draw comparisons in furniture types and distribution networks. Then it examines the shared presence of objects and assemblages from other first-century BCE shipwrecks using network analysis. Viewing shipwrecks as nested sets of assemblages combines close analysis of singular wrecks with aggregate data from multiple shipwrecks in an interconnected interpretive framework. The resulting network serves as a starting point for understanding the circulation of objects and facilitating interpretation of shipwrecks, ultimately refining our view of the acquisition of luxury objects in the western Mediterranean during the first century BCE. Finding that luxury objects, such as klinai, were being shipped not only to Italy but also around the same time to the western Mediterranean shifts the focus of study from Italic consumers to wider integrated transportation networks.1

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.628
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.005
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.029
GPT teacher head0.255
Teacher spread0.226 · 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.

Study designQualitative
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

Citations1
Published2024
Admission routes1
Has abstractyes

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