Directing Wayfinders With Maps: The Effects of Gender, Age, Route Complexity, and Familiarity With the Environment
Bibliographic record
Abstract
The participants were 360 Canadian undergraduates. After receiving written scenarios involving a campus visitor seeking direction to a nearby destination (simple route) or a distant destination (complex route), they drew maps to that destination. The authors varied the visitor's gender, age, and familiarity with campus. They analyzed the content of the students' maps in terms of cardial indicators (compass grid, correct north-south orientation), landmarks, labeled buildings, directional arrows, and supplemental written directions. The men tended to provide more cartographically complete maps than the women, though there were no gender differences in use of landmarks or labeled buildings. The men were significantly more inclined than the women to take visitor characteristics into account, providing more complete maps to visitors navigating complex routes, to newcomers, and to older visitors who were unfamiliar with the campus. The men had more confidence that their maps would successfully lead visitors to their destinations. Route complexity led to greater use of landmarks, labeled buildings, and cardinal indicators. The participants' self-reported familiarity with campus had little effect on their direction giving, except for a greater use of labeled buildings in maps.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".