Navigation Tasks with Small-Display Maps: The Sum of the Parts Does Not Equal the Whole
Bibliographic record
Abstract
A key strength of a map for navigation is that it can show the features of an environment and their spatial relationships over an area too large to be perceived through direct experience. This characteristic is important for navigation, including planning travel, making decisions while en route, and developing a user's cognitive map. But what happens on a small display, such as that of a cell phone, when, instead of seeing the entire area of interest, the user can view only a discrete section of the map at any time? This article presents research investigating the implications of a small display for map use and spatial knowledge acquisition. A research study was conducted with 80 participants using a map to answer distance and direction questions in one of four viewable-extent conditions, ranging from 10% to 100% of the map viewable at a time. Map-use results showed that small viewable extents hindered performance with respect to accuracy and response time but had no effect on participants’ confidence in their performance on the navigation tasks. Tests of spatial knowledge acquisition showed differences across conditions for recall tasks, but a sketch-map analysis revealed no differences based on viewable extent.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".