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
Even though personal firewalls are an important aspect of security for the users of personal computers, little attention has been given to their usability. We conducted semi-structured interviews with a diverse set of participants to gain an understanding of their knowledge, requirements, perceptions, and misconceptions of personal firewalls. Through a qualitative analysis of the data, we found that most of our participants were not aware of the functionality of personal firewalls and their role in protecting computers. Most of our participants required different levels of protection from their personal firewalls in different contexts. The most important factors that affect their requirements are their activity, the network settings, and the people in the network. The requirements and preferences for their interaction with a personal firewall varied based on their levels of security knowledge and expertise. We discuss implications of our results for the design of personal firewalls. We recommend integrating the personal firewall with other security applications, adjusting its behavior based on users' levels of security knowledge, and providing different levels of protection based on context. We also provide implications for automating personal firewall decisions and designing better warnings and notices.
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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