Bibliographic record
Abstract
Some researches in education science develop educative games on mobile devices for letting elementary school students play outdoor to learn geographic facts, concepts, and patterns. The challenge is about improving their geographic literacy and fluency, or ‘geoliteracy’, and their map-reading competencies, called cartology, before adolescence. There a critical stumbling ‘threshold’ can impede their geospatial cognitive development, which result in a majority of adults being not geographically literate neither efficient, in real-life context, for reading and using maps. Designing a mobile educative serious game implies applying conceptual and pragmatic methods for both learning and teaching geospatial competencies accordingly to school curriculum. The theoretical framework presented links maps to cartographical semiology, the children’s cognitive development stages for geospatial representation, and an experiential learning cycle model. The latter sequentially supports three main cartographic processes of map-making: reflexive visualization, and map-reading, which sustain any geographical reasoning. The mobile game proposed combines components of increasing complexity where the map plays the main role in the course of different activities: scenarios of typical “rounds” and rules of the game within local terrain; types of geometrical and geospatial trajectories to trace and follow while playing; and specific themes relevant to school subjects. Thus, geographical discussions stop worrying about where, to worry about the reason of situations and the occurrence of phenomena.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".