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
The number of computing devices that people use is growing. To gain a better understanding of why and how people use multiple devices, we interviewed 27 people from academia and industry. From these interviews we distill four primary findings. First, associating a user's activities with a particular device is problematic for multiple device users because many activities span multiple devices. Second, device use varies by user and circumstance; users assign different roles to devices both by choice and by constraint. Third, users in industry want to separate work and personal activities across work and personal devices, but they have difficulty doing so in practice Finally, users employ a variety of techniques for accessing information across devices, but there is room for improvement: participants reported managing information across their devices as the most challenging aspect of using multiple devices. We suggest opportunities to improve the user experience by focusing on the user rather than the applications and devices; making devices aware of their roles; and providing lighter-weight methods for transferring information, including synchronization services that engender more trust from users.
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 | 0.000 | 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) | 0.006 | 0.013 |
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