Bibliographic record
Abstract
Computers are now ubiquitous. However, computers and digital content have remained largely separate from the physical world - users explicitly interact with computers through small screens and input devices, and the "virtual world" of digital content has had very little overlap with the practical, physical world. My thesis work is concerned with helping computing escape the confines of screens and devices, spilling digital content out onto the physical world around us. In this way, I aim to help bridge the gap between the information-rich digital world and the familiar environment of the physical world and allow users to interact with digital content as they would ordinary physical content. I approach this problem from many angles: from the low-level work of providing high-fidelity touch interaction on everyday surfaces, easily transforming these surfaces into enormous touchscreens; to high-level questions surrounding the interaction design between physical and virtual realms. To achieve this end, building on my prior work, I developed two physical embodiments of this new mixed-reality design: a tiny, miniaturized projector and camera system providing the hardware basis for a projected on-world interface, and a head-mounted augmented-reality head-mounted display modified to support touch interaction on arbitrary surfaces.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.426 | 0.371 |
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".