The application of network analysis to ancient transport geography: A case study of Roman Baetica
Bibliographic record
Abstract
In many ways the Roman province of Baetica is an ideal subject for exploring new approaches to historic transport geography. This is not due to the completeness of its record (for it is not), but because it provides a remarkable breadth of pertinent data. This paper, loosely based on a seminar hosted by the Digital Classicist at King’s College London, will briefly discuss the results of applying some as-yet relatively uncommon techniques to the archaeology and documentary record of transport in the area. It will then go on to tackle some more general issues in creating maps of movement in the past, concluding that there is still much theoretical work to be done, but that the potential for discovering new patterns in old data is great, and indeed, ever growing. The main concept that will be explored is that of a Node Network, an abstract model of the interactions between spatially separate locations. This paper demonstrates the potential of a standard relational database, coupled with a GIS and Network Analysis software package, to make a spatial argument about the relative importance of key towns within a transport network and expose the constituent elements of that argument in a formal, visual manner.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".