The Effect of Global-Scale Map-Projection Knowledge on Perceived Land Area
Bibliographic record
Abstract
The acquisition and conceptualization of spatial knowledge are important topics in human spatial cognition. At the global scale, maps are our primary graphic source of information; however, they distort the size and shape of geographic features. If a distorted reference is used and the reader assumes it to be accurate, it may inappropriately influence decision making and, possibly, the shape of our global-scale cognitive maps. This paper examines trends in perception of land area, using equal-area and non-equal-area references, as well as investigating how map-projection knowledge can influence interpretation of land area. Results from the land-area studies show that map readers attempted compensation for projection distortion only when using the Mercator projection as a reference, and only for certain regions displayed on the Mercator projection. For other reference materials there is no attempted compensation for perceived distortion, even when participants believe that the reference is distorting land area. It is also apparent that most participants have limited projection knowledge and have difficulty transferring this knowledge to other projections or to practical application tasks. Both of these findings have implications for understanding perceptual issues in map reading and for determining where distortions can be introduced at the encoding stage of cognitive map development.
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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 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".