The Impact of 1980s and 1990s Video Games on Multimedia Cartography
Bibliographic record
Abstract
The video game industry revolutionized the game market from the 1970s onwards. Stationary video game machines, such as “coin-ops” and, later, consoles for home entertainment made it possible to experience and interact with new virtual environments. Based on technical innovations, early video games already included different graphic and auditory effects that were used to present and emphasize the spatial dimension of game stories. One of the most famous and successful video game series that “told” spatial stories and included many visualizations of virtual topographies was Nintendo's Super Mario series. Nintendo developed diverse video game topographies including different interactive and animated cartographic media throughout the Super Mario series. These maps were early and fundamental examples that were user-friendly and suitable for children. Moreover, they established a basis for future video game spaces, and the techniques used to create, animate, and visualize these maps have also found their ways into other applications of cartography and geomatics. It seems that the early worlds of Super Mario animated cartographers to animate cartographic visualizations. This article presents the characteristic spatial structures and cartographic techniques found in early Super Mario games, from the arcade classic Donkey Kong (1981) to the Super Nintendo classic Super Mario Kart (1992). The meaning of these structures and techniques for other cartographic applications is discussed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".