Diagramming information structures using 3D perceptual primitives
Bibliographic record
Abstract
The class of diagrams known collectively as node-link diagrams are used extensively for many applications, including planning, communications networks, and computer software. The defining features of these diagrams are nodes, represented by a circle or rectangle connected by links usually represented by some form of line or arrow. We investigate the proposition that drawing three-dimensional shaded elements instead of using simple lines and outlines will result in diagrams that are easier to interpret. A set of guidelines for such diagrams is derived from perception theory and these collectively define the concept of the geon diagram. We also introduce a new substructure identification task for evaluating diagrams and use it to test the effectiveness of geon diagrams. The results from five experiments are reported. In the first three experiments geon diagrams are compared to Unified Modeling Language (UML) diagrams. The results show that substructures can be identified in geon diagrams with approximately half the errors and significantly faster. The results also show that geon diagrams can be recalled much more reliably than structurally equivalent UML diagrams. In the final two experiments geon diagrams are compared with diagrams having the same outline but not constructed with shaded solids. This is designed to specifically test the importance of using 3D shaded primitives. The results also show that substructures can be identified much more accurately with shaded components than with 2D outline equivalents and remembered more reliably. Implications for the design of diagrams are discussed.
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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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".