Heuristics for information visualization evaluation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Heuristic evaluation is a well known discount evaluation technique in human-computer interaction (HCI) but has not been utilized in information visualization (InfoVis) to the same extent. While several sets of heuristics have been used or proposed for InfoVis, it is not yet known what kind of heuristics are useful for finding general InfoVis problems. We performed a meta-analysis with the goal of exploring the issues of heuristic evaluation for InfoVis. This meta-analysis concentrates on issues pertaining to the selection and organization of heuristics, and the process itself. For this purpose, we used three sets of previously published heuristics to assess a visual decision support system that is used to examine simulation data. The meta-analysis shows that the evaluation process and results have a high dependency on the heuristics and the types of evaluators chosen. We describe issues related to interpretation, redundancy, and conflict in heuristics. We also provide a discussion of generalizability and categorization of these heuristics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it