Increasing the utility of quantitative empirical studies for meta-analysis
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
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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