Cartography and Code: Incorporating Automation in the Exploration of Medieval Mappaemundi
Bibliographic record
Abstract
The study of medieval mappaemundi has traditionally relied on comparative analysis as one of the methods to examine and identify possible centres of map production, common sources and conceptions of space and place, as well as possible social, institutional, ideological, and economic connections between maps. The comparison of place names between medieval mappaemundi is one of the ways scholars can compare toponyms across maps, but this has, until now, been conducted by hand, usually on a case by case study. This paper introduces a new digital tool called veccompare, designed to facilitate an in-depth study of the relationships between mappaemundi vis à vis their textual content. As a case study, veccompare has been used for the comparison of two Psalter maps. Veccompare’s output report and visualization of data has led to a better understanding of the Psalter maps’ close physical proximity, namely on the same folio in the same manuscript. It may be pursuant not to a direct, one-to-one, model-derivative type of relationship between the maps, but rather to an indirect relationship stemming from a common textual source used by both mapmakers.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".