Bibliographic record
Abstract
Tabletop computing techniques are using physically familiar force-based interactions to enable compelling interfaces that provide a feeling of being embodied with a virtual object. We introduce an interaction paradigm that has the benefits of force-based interaction complete with full 6DOF manipulation. Only multi-touch input, such as that provided by the Microsoft Surface and the SMART Table, is necessary to achieve this interaction freedom. This paradigm is realized through sticky tools: a combination of sticky fingers, a physically familiar technique for moving, spinning, and lifting virtual objects; opposable thumbs, a method for flipping objects over; and virtual tools, a method for propagating behaviour to other virtual objects in the scene. We show how sticky tools can introduce richer meaning to tabletop computing by drawing a parallel between sticky tools and the discussion in Urp [20] around the meaning of tangible devices in terms of nouns, verbs, reconfigurable tools, attributes, and pure objects. We then relate this discussion to other force-based interaction techniques by describing how a designer can introduce complexity in how people can control both physical and virtual objects, how physical objects can control both physical and virtual objects, and how virtual objects can control virtual objects.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.007 |
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".