Nature of Old Maps: As Primary Source Materials for Historical Geography
Bibliographic record
Abstract
This paper discusses on the meaning and the nature of old maps of both small and large scales for historical geography. The old map can be an object of research itself as well as a source for research on something else. But both research directions have been closely connected with each other, and also each research direction needs inevitably another. Roughly speaking, small scale old maps mean mainly world maps, although they have no accurate scale. They commonly reflect the enormous expansion of geographical knowledge acquired during periods of exploration of little-known places, and they clearly reflect the perception of space that pertained at the time when the map was drawn. In case of large scale maps like manorial maps and town/village maps as cadastral ones, they express much local information in which local people interested or according to the land system or land planning in different times. Some large/middle scale maps show more formal land planning, those showing grid land planning like Centuria in Roman Empire, Jori Plan in 8-9th century Japan and Townships in British American colonies and later US and Canada. Those large/middle scale maps usually express various land units within each grid in spite of a land itself usually stretched continuously. Old maps are very important as primary source materials for historical geography, but researchers should consider the nature of old maps. They are commonly without accurate scale, physical situation and standard for drawing and describing. And furthermore, many old maps were made under the thought or regulation for land planning, when those are used as source materials.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.019 | 0.024 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".