Supporting Lightweight Customization for Meeting Environments
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.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".