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
Mobile devices have become powerful ultra-portable personal computers supporting not only communication but also running a variety of complex, interactive applications. Because of the unique characteristics of mobile interaction, a better understanding of the time duration and context of mobile device uses could help to improve and streamline the user experience. In this paper, we first explore the anatomy of mobile device use and propose a classification of use based on duration and interaction type: glance, review, and engage. We then focus our investigation on short review interactions and identify opportunities for streamlining these mobile device uses through proactively suggesting short tasks to the user that go beyond simple application notifications. We evaluate the concept through a user evaluation of an interactive lock screen prototype, called ProactiveTasks. We use the findings from our study to create and explore the design space for proactively presenting tasks to the users. Our findings underline the need for a more nuanced set of interactions that support short mobile device uses, in particular review sessions.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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