Strategies for Bridging the Gap between Analysis and Design for Ecological Interface Design
Bibliographic record
Abstract
Creating effective graphic displays using Ecological Interface Design (EID) can be a challenging endeavor. Thus far, there has been little guidance in the literature to decrease the gap that exists between EID analysis and design. This paper presents strategies to help bridge this gap. First, a visual thesaurus provides alternative graphic objects and display formats for showing single variables, single-variable constraints, multivariate constraints, and structural constraints (i.e., means-ends, part-whole, and causal/topological relationships). Second, a number of lessons learned have been identified to improve efficiency in the process and provide further refinements to the display design. These strategies can decrease the effort involved in creating EID displays, but do not completely remove creativity from the design process. Future research includes further developing generic and industry-specific graphic forms for the visual thesaurus, while embedding effective design practices.
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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 | 0.063 | 0.096 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".