Role-based control of shared application views
Bibliographic record
Abstract
Collaboration often relies on all group members having a shared view of a single-user application. A common situation is a single active presenter sharing a live view of her workstation screen with a passive audience, using simple hardware-based video signal projection onto a large screen or simple bitmap-based sharing protocols. This offers simplicity and some advantages over more sophisticated software-based replication solutions, but everyone has the exact same view of the application. This conflicts with the presenter's need to keep some information and interaction details private. It also fails to recognize the needs of the passive audience, who may struggle to follow the presentation because of verbosity, display clutter or insufficient familiarity with the application.Views that cater to the different roles of the presenter and the audience can be provided by custom solutions, but these tend to be bound to a particular application. In this paper we describe a general technique and implementation details of a prototype system that allows standardized role-specific views of existing single-user applications and permits additional customization that is application-specific with no change to the application source code. Role-based policies control manipulation and display of shared windows and image buffers produced by the application, providing semi-automated privacy protection and relaxed verbosity to meet both presenter and audience needs.
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 | 0.021 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".