Digital Sketch-Map Drawing as an Instrument to Collect Data about Spatial Cognition
Bibliographic record
Abstract
The formative years of cognitive mapping research focused on theoretical understanding, with less emphasis on developing innovative methodologies to extract cognitive maps. By the 1990s, new cross-disciplinary exchanges with computer science and information technology had renewed interest in the field. This article describes a method for collecting, mapping, and exploring the sequence of sketch-map creation, including integration of the resulting sketch maps into a geographic information system (GIS) for visualization and potential geometric analyses. The method involves the use of a tablet computer that allowed subjects to draw their sketch maps directly onscreen while computer software simultaneously records the drawing process in audio and video format. Results from a pilot study with 45 participants demonstrate that the method preserves the quality of drawn sketch maps but adds several new data elements and insights. In particular, the audio data were used to add labels and other attributes to drawn sketch-map elements, whereas the video data allowed tracking of the sequence in which elements are drawn. Analysis shows that paths tend to be drawn more frequently at first but soon decrease in frequency in favour of landmarks. Nodes, boundaries, and districts tend to be drawn throughout the drawing process but are much less frequent. Explanation and implications of these findings are discussed with respect to past methods and theories.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".