Towards a New Generation of Digital Cartography: The Development of Neocartography and the Geoweb
Bibliographic record
Abstract
The Third Industrial Revolution evolved through the development of technology in the 1960s and has been mirrored in other professions as well as cartography. The first maps on the Internet for mass use appeared in the late 1990s. In the beginning, they were simple and modest. Computer development was also reflected in the development of digital cartography, and maps become interactive with users. Technology has made collecting spatial data easier and cheaper, and cartography has become available to ordinary users through various tools and services. Accordingly, emerging concepts and terms related to digital cartography are sometimes identical or match part of their domain of meaning. This article offers a review and analysis of keywords pertaining to digital cartography on the Internet. Different indicators are used to show trends in selected keywords’ appearance and, thus, trends in 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".