Maritime Networks, Connectivity, and Mobility in the Ancient Mediterranean
Bibliographic record
Abstract
In an exponentially hyper-connected modern world it is tempting to imagine that the past was a different place, one of sedentary villages in which most people barely ventured beyond familiar confines.Indeed, for Mediterranean prehistory, it is farms and hamlets that dominate the settlement record (Whitelaw 2017, 118).One might, then, easily assume that in such societies most interactions were with family and neighbors, and of a frequency and regularity that made for an almost intuitive communication.In the study of antiquity this perspective is perhaps best encapsulated in Finley's assertion that ancient societies must have operated primarily on a face-to-face basis (Finley 1973).With this notion of the face-to-face, it is all too easy to portray society as static (Moatti 2006; see also Osborne 2011, 217).Mobility becomes an optional add-on, something that might well have happened, but certainly not an inherent societal condition (Clifford 1997).A strong response to this sedentarist bias emerged in the form of a so-called "mobility turn" that put movement center stage (Clifford 1997; Moatti 2004;Cresswell 2011).What are the implications of a perspective privileging mobility for the study of antiquity?That there was considerable movement in the ancient Mediterranean is hardly in doubt; it is quite clear from written sources and artifact distributions (de Ligt and Tacoma 2016).Furthermore, the sense of it being a precondition for Mediterranean life emerges once one takes into 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".