Spatialized information visualizations: a “BASSTEP” approach to application design
Bibliographic record
Abstract
Abstract Large result sets stemming from topical queries on the Web tend to cause disorientation and are often reduced to a manageable size through successive exclusions (NOTing) and/or restrictions (ANDing) which also reject relevant documents. The information visualization (IV) technique known as spatialization is a scalable automatic process creating a topographical map metaphor of the semantic information space. The domain of IV offers many prototypes, some controlled experiments but few low‐cost testing methods which are critical in the early stages of the design process. This communication reports on efforts to adapt the low‐cost BASSTEP approach to the evaluation of the IV technique known as spatialization by isolating and evaluating (using paper mock‐ups) information features of a spatialized Web corpus in the context of Web IR. Six (6) semi‐structured formative interviews provide a list of most expected initial user interpretations of four (4) features and three (3) additional interviews attempt to triangulate the findings when these features are integrated into a single interface mock‐up. This work hopes to verify if the application of the BASSTEP approach is applicable to spatialized visualisations early in the design process improving the usability of this visual IR tool.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".