Investigating Direct Manipulation of Graphical Encodings as a Method for\n User Interaction
Bibliographic record
Abstract
We investigate direct manipulation of graphical encodings as a method for\ninteracting with visualizations. There is an increasing interest in developing\nvisualization tools that enable users to perform operations by directly\nmanipulating graphical encodings rather than external widgets such as\ncheckboxes and sliders. Designers of such tools must decide which direct\nmanipulation operations should be supported, and identify how each operation\ncan be invoked. However, we lack empirical guidelines for how people convey\ntheir intended operations using direct manipulation of graphical encodings. We\naddress this issue by conducting a qualitative study that examines how\nparticipants perform 15 operations using direct manipulation of standard\ngraphical encodings. From this study, we 1) identify a list of strategies\npeople employ to perform each operation, 2) observe commonalities in strategies\nacross operations, and 3) derive implications to help designers leverage direct\nmanipulation of graphical encoding as a method for user interaction.\n
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.
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 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".