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
Most commercial software applications are designed for a single user using a keyboard/mouse over an upright monitor. Our interest is exploiting these systems so they work over a digital table. Mirroring what people do when working over traditional tables, we want to allow multiple people to interact naturally with the tabletop application and with each other via rich speech and hand gestures. In previous papers, we illustrated multi-user gesture and speech interaction on a digital table for geospatial applications -- Google Earth, Warcraft III and The Sims. In this paper, we describe our underlying architecture: GSI Demo. First, GSI Demo creates a run-time wrapper around existing single user applications: it accepts and translates speech and gestures from multiple people into a single stream of keyboard and mouse inputs recognized by the application. Second, it lets people use multimodal demonstration -- instead of programming -- to quickly map their own speech and gestures to these keyboard/mouse inputs. For example, continuous gestures are trained by saying "Computer, when I do [one finger gesture], you do [mouse drag]". Similarly, discrete speech commands can be trained by saying "Computer, when I say [layer bars], you do [keyboard and mouse macro]". The end result is that end users can rapidly transform single user commercial applications into a multi-user, multimodal digital tabletop system.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it