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
We explore mobile privacy through a survey and through usability evaluation of three privacy-preserving mobile applications. Our survey explores users' knowledge of privacy risks, as well as their attitudes and motivations to protect their privacy on mobile devices. We found that users have incomplete mental models of privacy risks associated with such devices. And, although participants believe they are primarily responsible for protecting their own privacy, there is a clear gap between their perceived privacy risks and the defenses they employ. For example, only 6% of participants use privacy-preserving applications on their mobile devices, but 83% are concerned about privacy. Our usability studies show that mobile privacy-preserving tools fail to fulfill fundamental usability goals such as learnability and intuitiveness---potential reasons for their low adoption rates. Through a better understanding of users' perception and attitude towards privacy risks, we aim to inform the design of privacy-preserving mobile applications. We look at these tools through users' eyes, and provide recommendations to improve their usability and increase user-acceptance.
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 | 0.001 |
| 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