Designing a Rule-based Wizard for Visualizing Statistical Data on Thematic Maps
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Thematic maps are used in a wide range of scientific fields to illustrate specific geographic phenomena. For their correct construction, the mapmaker has to select the appropriate data, and then consider different parameters and constraints in order to visualize them effectively. In this paper, these parameters were analyzed, so that a consistent and standardized workflow for producing thematic maps could be set up. This workflow served as the basis for designing and implementing a step-by-step wizard-based application. Its goal is to guide mapmakers—experts or laypersons—to create cartographically sound thematic maps based on statistical data, in a user-friendly way. To standardize the procedure, we analyzed the relationships between different mapping techniques and the types of data with which they are used to illustrate a geographic phenomenon. Based on this analysis, we created a new taxonomy of mapping techniques and used it to automate the selection procedure within the wizard. This analysis could also be of general use for researchers producing thematic maps in different mapping applications.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it