Gender Differences in the Sketch Map Creation Process
Bibliographic record
Abstract
Gender differences in navigation and mapping skills have long been noted in past research. Females tend to use landmarks for navigation while males favour paths and co-ordinate systems. Similar findings are found in sketch maps, where landmarks are prevalent on female maps and males draw more paths. Although the type and quantity of map elements give an indication of gender differences, there is potential for further insights to emerge from analysis of the process or sequence in which map elements are drawn. The sequence provides detail on when in the drawing process gender differences or similarities in map elements arise. This study found that in the initial drawing of the map, there were few gender difference in the number of landmarks and paths drawn. However, females drew a larger proportion of landmarks and males drew more paths from 10% to 25% into the drawing process. Thereon until half way through, no statistical differences were found. Differences were seen again in the last half of the drawing process. Overall, females drew more landmarks and paths than men, but the difference lay in when clusters of these map elements were drawn.
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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 | 0.021 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 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".