Testing Subjective Preference and Map Use Performance: Use of Web Maps for Decision Making in the Public Health Sector
Bibliographic record
Abstract
Maps support many planning activities, from general purpose tasks to in-depth spatial analyses. However, preferences and intuitions, which occur in all human interactions, are often disregarded in use and user research in cartography, and there is evidences that analysis of these factors is desirable. It is commonly believed that there is a positive correlation between subjective preference and objective performance, and there is agreement about the main role of media in map comprehension. To investigate this topic, quantitative experiments were performed, based on a real situation in which students need to make decisions about public health care management in a city. Users were asked about their preferences with respect to map type, and performances in carrying out tasks were measured. The results indicate that for simple visual comparison tasks, the proposed Web map framework was adequate. However, this was not the case for reasoning tasks, where weak performances were registered. Also, user preference among visual variables seemed to be unrelated to better performance, and since performances were poor, the important role played by interface usability and attractiveness in map use is verified.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".