Special Issue: Organic User Interfaces
Bibliographic record
Abstract
Until recently, the ways in which we interacted with computers were limited by the flat, rigid and rectangular form factors of cathode-ray tubes and liquid crystal displays. Technical limitations forced user interfaces into boxes, limiting interactions to 2D pointing and keyboard input. With technological advances in flexible sensor and display technologies, we are experiencing a new revolution in human–computer interaction (HCI): one in which user interfaces can be worn on the body as if they were cloth, used in the office as if they were paper and used in architecture as if they were wallpapers (Co and Pashenkov, 2008; Buechley and Mellis, 2010; Lahey et al., 2011). Rather than being rigid and static, the user interfaces of tomorrow will be able to have a shape that accommodates the user’s context and fits the data on display. For instance, if a user wants to explore geographic information, they can use a spherical display that does not require distortion of the earth projection (Stevenson, 2010). When reading an interactive map, users can extend their mobile’s screen real estate by unfolding their pocket-size flexible e-paper display.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.253 | 0.118 |
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".