Increasing the utility of quantitative empirical studies for meta-analysis
Bibliographic record
Abstract
Despite the long history and consistent use of quantitative empirical methods to evaluate information visualization techniques and systems, our understanding of interface use remains incomplete. While there are inherent limitations to the method, such as the choice of task and data, we believe the utility of study results can be enhanced if they were amenable to meta-analysis. Based on our experience in extracting design guidelines from existing quantitative studies, we recommend improvements to both study design and reporting to promote meta-analysis: (1) Use comparable interfaces in terms of visual elements, information content and amount displayed, levels of data organization displayed, and interaction complexity; (2) Capture usage patterns in addition to overall performance measurements to better identify design tradeoffs; (3) Isolate and study interface factors instead of overall interface performance; and (4) Report more study details, either within the publications, or as supplementary materials.
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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 | 0.752 | 0.936 |
| Meta-epidemiology (narrow) | 0.010 | 0.008 |
| Meta-epidemiology (broad) | 0.025 | 0.044 |
| Bibliometrics | 0.048 | 0.050 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.017 | 0.027 |
| Open science | 0.010 | 0.016 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".