Diverse Uses of Historical Cartography : From Urban Analysis to the Educational Tool
Bibliographic record
Abstract
The research project lures attention to historical cartography as the fundamental material that needs to be collected, analysed, understood and used in the preservation management projects for enhancing widespread heritage. In a search for a methodological approach to understanding historical cartography, the study will begin with a map collection of various scales, periods and themes, to be analysed and compared inside the same coordinate system. Researchers are interested in developing an exact methodology for understanding and usability of historical cartography is raising. Nonetheless, there is a gap in connecting the analysis of historical maps with their use in preservation projects. The imposed question "what is a map?" is very complex and depends on many factors. Since the maps are present in various spheres of studies, to get the right image of what a map can represent and closely defining what a map is, one should examine different stands. Collecting and understanding the historical cartography with the developed methodology will lead to the part of digital humanities that concern managing metadata of historical document, georeferencing and digitalizing maps in the geographic information systems (GIS) respecting the standards of European Union (Europeana 2018). This project presents ways in which digitalization and systematization of these maps can foster public and professional engagement with heritage (Garcıa-Esparza et al., 2020), contributing to the UNESCO/UBC Vancouver declaration (2012) that points out the importance of digitalization of historical documents along with intangible qualities of urban spaces.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".