Design and Evaluation of Visualization Techniques to Facilitate Argument Exploration
Bibliographic record
Abstract
Abstract This paper reports the design and comparison of three visualizations to represent the structure and content within arguments. Arguments are artifacts of reasoning widely used across domains such as education, policy making, and science. Anargumentis made up of sequences of statements (premises) which can support or contradict each other, individually or in groups through Boolean operators. Understanding the resulting hierarchical structure of arguments while being able to read the arguments' text poses problems related to overview, detail, and navigation. Based on interviews with argument analysts we iteratively designed three techniques, each using combinations of tree visualizations (sunburst, icicle), content display (in‐situ, tooltip) and interactive navigation. Structured discussions with the analysts show benefits of each these techniques; for example, sunburst being good in presenting overview but showing arguments in‐situ is better than pop‐ups. A controlleduser study with 21 participants and three tasks shows complementary evidence suggesting that a sunburst with pop‐up for the content is the best trade‐off solution. Our results can inform visualizations within existing argument visualization tools and increase the visibility of ‘novel‐and‐effective’ visualizations in the argument visualization community.
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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 | 0.025 | 0.115 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".