WAMS: A Flexible API for Visual Workspaces Across Multiple Surfaces
Bibliographic record
Abstract
Applications that use multiple devices and surfaces provide new opportunities for innovative interaction -- but despite the wide variety of research that has been carried out on multi-surface systems, building these kinds of applications is still difficult. In particular, multi-surface apps that use interactive visual workspaces are complicated because current tools do not provide low-level access to a connected and interactive graphical canvas that is shown on different devices. This difficulty limits the explorations that designers and developers can carry out within the multi-surface design space. To address this problem, we have developed WAMS -- an open-source web-based toolkit that provides several programming abstractions for building visual-workspace applications across multiple surfaces. WAMS uses three main concepts -- a virtual visual workspace, views onto that workspace, and graphical workspace objects -- and provides support for connecting multiple devices, creating and manipulating objects, managing and laying out views, and handling events from multiple surfaces. WAMS simplifies the development of a wide variety of applications including composite display configurations, shared-workspace groupware, systems that place different UI elements onto different devices, and bring-your-own-device applications. We describe WAMS's main abstractions and concepts, provide several examples that show the breadth of the approach, and assess the toolkit in terms of effectiveness, coverage, extensibility, and integration with existing practices and tools.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.011 |
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".