Bibliographic record
Abstract
Unlike the traditional computer that is based on a screen, mouse and keyboard, physical interfaces for collaborative interaction are special purpose devices that can be situated in a real-world setting and are designed for particular collaborative contexts and uses. However this is a new design genre; developers do not yet know what these devices should do and what they should look like. In this chapter, I hint at the variety of design categories for collaborative physical interfaces, as suggested and illustrated by a collection of working prototypes created by researchers and students at the University of Calgary. We will also see that two toolkits encouraged people to explore creative ideas in this new genre. Through Phidgets, people rapidly prototype physical user interfaces under computer control. Through the Grouplab Collabrary, people had an easy means to share data between interconnected devices and software. These combined tools became a media form that allowed researchers and students to create and develop new ideas concerning physical interfaces for collaborative interaction, or to vary already established ideas in interesting ways.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.077 | 0.017 |
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".