Bibliographic record
Abstract
The use of the newest mobile devices, especially by young people and children, is constantly growing in many countries. The youngest generations consider the use of smartphones and tablets as a “natural tool” to help them in their daily activities. The wider use of these devices is determined by the increasing presence of location-based services and Web 2.0–based applications. Most map-based applications developed for smartphones are location-based services intended to help the users orient themselves or seek thematic (e.g., tourist) information in a given environment. This study presents the possibilities of using smartphones in school cartography, specifically for displaying school atlases. After a brief background on the use of smartphones by children and young people in different countries, a short introduction on digital atlases in general is given, followed by our recommendations for the adoption of school atlases for these devices. We describe the two initial aspects of the process of adaptation of these atlases for smartphones: the adaptation of content and of (carto)graphic solutions. Finally, we discuss whether the mobile device–based school atlases can be improved by combining solutions used in digital atlases with new solutions developed for mobile devices, such as improved location-based service technology or 3D technology-based representations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".