Supporting Lightweight Customization for Meeting Environments
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
Digital wall-sized displays commonly support authoring and presentation in face to face meetings. Yet most meeting applications show not only meeting content (i.e., the mate-rial being developed) but authoring tools as well the usual controls, palettes, and menus. Attendees are dis-tracted when the author navigates the (usually complex) interface as part of the authoring process the tools them-selves unnecessarily clutter the display. The problem is that current customization techniques are not suited for meeting environments as complex customization interfaces take attention away from the meeting agenda thus making cus-tomization a socially unacceptable practice. In this paper, we present the solution of lightweight cus-tomization, a customization technique designed to mini-mize time and cognitive effort. This paper illustrates lightweight customization through two implementations: First, customized views provide a scribe with full applica-tion functionality while presenting the important presenta-tion content to the other meeting collaborators on a secon-dary projected display. Second, customized interfaces al-low meeting collaborators to rapidly recall previous func-tionality and build customized interfaces through a history of previous actions.
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 | 0.001 |
| Open science | 0.001 | 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