RAPIDLY PROTOTYPING SINGLE DISPLAY GROUPWARE THROUGH THE SDGTOOLKIT
Bibliographic record
Abstract
Researchers in Single Display Goupware (SDG) explore how multiple users share a single display such as a computer monitor, a large wall display, or an electronic tabletop display. Yet todays personal computers are designed with the assumption that one person interacts with the display at a time. Thus researchers and programmers face considerable hurdles if they wish to develop SDG. Our solution is the SDGToolkit, a toolkit for rapidly prototyping SDG. SDGToolkit automatically captures and manages multiple mice and keyboards, and presents them to the programmer as uniquely identified input events relative to either the whole screen or a particular window. It transparently provides multiple cursors, one for each mouse. To handle orientation issues for tabletop displays (i.e., people seated across from one another), programmers can specify a participants seating angle, which automatically rotates the cursor and translates input coordinates so the mouse behaves correctly. Finally, SDGToolkit provides an SDG-aware widget class layer that significantly eases how programmers create novel graphical components that recognize and respond to multiple inputs.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".