Ubiquitous Computing for Capture and Access
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
People may want to recall a multitude of experiences and information from everyday life. Human memory, however, has its limitations and can be insufficient for capturing and allowing access to salient information and important details over time. A variety of tools — primitive, analog, or digital — can complement natural memories through recording. Throughout history, in fact, record keeping and documentation have become increasingly important. In recent years, ubiquitous computing researchers have also designed and constructed mechanisms to support people in gathering, archiving, and retrieving these artifacts, a broad class of applications known as capture and access. In this paper, we overview the history of documentation and recording leading broadly from primitive tools into the current age of ubiquitous computing and automatic or semi-automatic recording technologies. We present historical visions motivating much of the early computing research in this area. We then outline the key problems that have been explored in the last three decades. Additionally, we chart future research directions and potential new focus areas in this space. This paper is based on a comprehensive analysis of the literature and both our experiences and those of many of our colleagues.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it