Effective presentation of ontological overlap of multiple conceptual models
Bibliographic record
Abstract
Conceptual models are used to help professionals understand complex information systems and solve problems during systems analysis and design. Because single model often do not represent all relevant information, typically multiple models are used in combination. To design effective combinations of models, we propose a systematic approach that uses color highlighting to foreground overlapping concepts between multiple models to help readers identify corresponding information between models. We conducted two empirical studies – an online experiment and an eye-tracking experiment – to evaluate the cognitive efficacy of this approach. Our findings suggest that color highlighting can somewhat improve participants’ domain understanding but not the efficiency of problem-solving. Findings from the eye-tracking study suggest that the use of color can have both beneficial and harmful effects, depending on the extent of overlap. • Color highlighting aids the perceptual integration and synchronization of overlap between multiple conceptual models. • Color highlighting can improve information integration processes, which in some cases aids problem-solving. • Color highlighting has both positive and negative effects when it comes to the information search processes.
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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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".