Bibliographic record
Abstract
Choropleth maps are commonly used to show statistical variation among map enumeration units. Mapmakers take into account numerous considerations and make many decisions to produce a product that will effectively communicate spatially complex information to the map user. One design consideration is the choice between classed or unclassed choropleth maps. Unclassed maps assign a unique color, shade, or pattern based on each unit's value. These maps are rich in information but might not be optimal for visual discrimination of regions or identifying values from a legend. Classed maps classify enumeration units based on unit values and in some cases consider geographic area per class or contiguity. These classed maps better delineate regions and interclass variation but are designed to eliminate visibility of intraclass variations. We present a method designed to use colors for choropleth classes and soft shadows to show intraclass variations associated with adjacent or nearby polygons. We conceptualize the choropleth data as a three-dimensional prism model under simulated illumination, with the height of each enumeration unit a function of its mapped value. Our user studies have demonstrated that participants were able to use soft shadows to better identify which of two adjacent units was of greater population density, regardless of whether units were in the same or different classes. Additionally, the resulting soft shadows rarely interfere with the map reader's ability to match color classes to a legend or to compare estimated differences in mean and variance of population density between two regions.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".