Shipwreck Assemblages and Network Analysis: Reconstructing the Furniture Trade in the Mediterranean Using First-Century BCE Shipwrecks
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".