We Need to Talk (more) About Numeracy! Toward a Numeracy-Informed Visualization Design Process
Bibliographic record
Abstract
Numeracy—the ability to understand and reason with numbers—is essential for interpreting and acting on quantitative information, yet it remains under-addressed in visualization research. In this position paper, we argue that numeracy should be a guiding principle in the design and evaluation of public-facing visualizations. Drawing on cognitive psychology and decision science, we propose a numeracy-informed design process: (1) measuring individual differences in numeracy, (2) designing visualizations that support gist and verbatim understanding, reduce cognitive effort, and provide evaluative meaning, and (3) evaluating quantitative comprehension across numeracy levels. Our goal is to spark a broader conversation about inclusive visualization practices that reflect numerical diversity and support public understanding of quantitative information.
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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 | 0.055 | 0.183 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.015 | 0.031 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".