Maps and Beyond: An Excursus on Sixteenth-Century Maps of American Coastlines, Collected in Italy, a GIS Approach
Bibliographic record
Abstract
In the late fifteenth and throughout the sixteenth century, European intellectual, scientific, and industrial influences manifested themselves brilliantly in the fields of geography, cartography, and printing and in improving maritime technologies. These areas of expertise came together in a fashion that made possible a rapid transference of information, in printed form, of the discoveries that Italian, Iberian, and later French, Dutch, and English explorers made. At that time, European scholars, wealthy patrons, and political and business figures attempted to understand the new discoveries, such as the Americas, through charts. In this article, the Italian cartographers and map publishers are considered to have shown a remarkable ability to depict North America, the challenge of placing these documents in the contemporary context is met through a GIS that solves the difficulty of viewing them through the rear-view mirror, 500 years later. The time frame chosen is from 1502 to 1536. Through a process of comparing and adjusting the original old maps with current maps, the GIS approach shows a geographical overview of the navigator's consciousness. The study tackles the time considered by studying five Italian maps and one Spanish map: the maps of Cantino, Maggiolo, Oliveriana of Pesaro, Castiglioni, Verrazzano, and Battista Agnese. Importing the maps into a geodatabase revealed significant geographical differences.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".