Bibliographic record
Abstract
Our research goal is to facilitate the sharing of stories with digital maps. Today, many kinds of “my map” applications allow users to create maps that display personal collections of places visited, along with photographs, videos, and texts. The authors of such maps can organize and publish their collections of places by creating maps, and viewers can browse these collections freely by panning and zooming the maps or searching by location. It is difficult, however, for authors to represent their experiences as a sequence of events, mainly because it is up to the viewer to decide which map locations will be viewed, and in what order. Therefore, one future requirement for this type of map application is to facilitate map-based storytelling. The objective of this study is to identify and discuss the characteristics of map-based stories and the effectiveness of maps in editing them. To help users communicate map-based stories in a more narrative fashion, we have developed software for mapping photo collections and creating slideshows to present travelogues, sightseeing guides, and so on, which we call a map-based slideshow. In this article, we analyse user-created, map-based slideshows, focusing on the spatiotemporal relationships among the photographs that compose the slideshow. We also describe a user study focusing on how and in which order users edit map-based slideshows. The results show characteristics of the spatiotemporal structures of map-based stories. We also evaluated the effectiveness of maps in the user's story-editing process. Because the spatial slideshow is a typical form of map-based story, we assume that most of the knowledge acquired is applicable not only to map-based slideshows but also to other map-based stories, such as a “tour” of Google Earth.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".