Embodied User Interfaces for Really Direct Manipulation
Bibliographic record
Abstract
esearch efforts exploring augmented reality: Hiroshi Ishii's Tangible Bits project at the MIT Media Lab [4], Jun Rekimoto's Augmented Reality project at Sony Research Labs [5], George Fitzmaurice's Graspable User Interface research at Alias|Wavefront and University of Toronto [2], as well as our own work [1, 3]. We also see this direction being pursued in the marketplace in portable computational "appliances," such as handheld devices (PDAs, most notably the Palm series of handhelds) and the recent wave of electronic books or e-books (for example, the SoftBook). Several features of these new devices are noteworthy: . The devices are portable and graspable: they must be held, touched, and carried to be used. . They are designed to best support a limited set of specific tasks. Embodied User Interfaces for Really Direct Manipulation Treating thi body of th h device as part of its user interface. Kenneth P. Fishkin, Anuj Gujar,
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.006 |
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".