Bibliographic record
Abstract
Compared to static maps, animated maps have always been difficult to make, distribute, and access. The PC and Internet revolutions have greatly improved opportunities for animated maps, and a new era of on-demand animated maps is emerging. In this article, a three-tier historical framework is presented that identifies the key conceptual and technological developments of animated cartography related to the means of production, methods of distribution, and modes of use. Such a historical overview is largely missing from the cartographic literature and helps to situate current developments and issues within a broader social and technological context. The future development and direction of animated maps can be informed by identifying the pivotal ideas and technologies of the last 60 years. It is worth revisiting foundational — but technologically impractical — ideas for animated maps developed by pioneering cartographers in the pre-digital and pre-Web era. This research also includes a look at important remaining technological and conceptual hurdles in the production and distribution of animated maps (e.g., bandwidth, vector-based animation, and automated production) and examines future prospects for on-demand animated mapping systems.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".