StencilMaps and EphemeralMaps: spatially stable interfaces that highlight command subsets
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
Identifying a target command can be difficult and time-consuming when the user is unfamiliar with a software system. One technique for assisting command identification is to provide a subset interface that contains only a limited set of the system's capabilities. We examine the design of subset interfaces, showing that subsets can be presented separately to the full user interface (UI) (e.g. in a palette) or in place, with in-place methods using either static or dynamic methods to identify the subset. We introduce the StencilMap and EphemeralMap as in-place subset UIs that, respectively, use static and dynamic highlighting. Both StencilMaps and EphemeralMaps make all of an application's commands concurrently available for selection within a grid. To highlight subset items StencilMaps use a static dark semi-transparent ‘stencil’ overlay to de-emphasise all but the subset items; EphemeralMaps, in contrast, use a short delay, with subset items shown immediately, and other items gradually faded in. A first experiment compares user performance with the in-place presentation of StencilMaps against that of the separate presentation of a subset palette. Results confirm the predicted spatial memory benefits for StencilMaps. A second experiment analyses the performance impact of three approaches to highlighting: none, static highlighting in StencilMaps, and dynamic highlighting in EphemeralMaps. Results show an interesting trade-off – while highlighting can offer benefits in assisting rapid target identification (particularly when the user is unfamiliar with the interface layout), there can also be longer-term performance benefits when highlighting is absent because the increased difficulty of visual search promotes the use and formation of spatial memory.
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 | 0.004 |
| 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