What Do Children Draw When Asked to Draw a Map? Results of a Mental Map Experiment
Bibliographic record
Abstract
When asked to draw a map reflecting on their experience, what do children draw? The authors offer possible answers through the eyes of children aged 6 to 14 who visited the Meadows Center for Water and the Environment on the campus of Texas State University; the 332 maps children were asked to draw after their visit are the focus. Results indicate that according to children, a map can be qualitatively understood as a graphic representation of the child’s experience that includes people and animals, places, and events, and natural and built environments. Children use both mimetic and abstract symbols that vary in shape, are often used repeatedly to create texture or patterns, and can vary in colour that often—but not always—abide by traditional colour denotations. Cartographic scales, legends, or north arrows are rarely used. The abundant use of written labels or descriptive words on their maps suggests that children understand maps as an expressive form that blends symbols and text. In efforts to contribute to the ultimate questioning of what makes a map a map, this study provides a strong empirical case for the what and how of children’s map-making processes concentrating on traditional cartographic conventions and elements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".