Cartographic Connections - the digital analysis and curation of sixteenth-century maps of Great Britain and Ireland
Bibliographic record
Abstract
Understanding the processes by which early maps were created and the interconnections of maps and map makers is key to broadening our knowledge of map history and cartographic science. This paper draws on data collected across three UK research projects centred on analysing, untangling and evaluating the relationships between maps and map makers of sixteenth-century Great Britain and Ireland. Using GIS, ‘Place’ features (written in Latin, Gaelic, Welsh and English) derived from a large suite of maps were digitised and added to one centralised geo-historical gazetteer. Employing robust quantitative methods including statistical regression procedures, distortion measures and displacement modelling, the maps were analysed and compared to reveal significant insights into the cartographic connections and the map making processes of Renaissance Europe. The paper also illustrates a common goal of these projects, to ‘curate’ early maps by enabling accessibility to cartography and associated data through online resources. The paper highlights that digital methods and curation, used in combination with more traditional forms of qualitative enquiry, provide a new instrument for deciphering and conserving histories of cartography.
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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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.018 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".