BALANCING PRIVACY AND AWARENESS FOR TELECOMMUTERS USING BLUR FILTRATION
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
Always-on video provides rich awareness for co-workers separated by distance, yet it has the potential to threaten privacy as sensitive details may be broadcast to others. This threat increases for telecommuters who work at home and connect to office-based colleagues using video. One technique for balancing privacy and awareness is blur filtration, which blurs video to hide sensitive details while still giving the viewer a sense of what is going on. While other researchers found that blur filtration mitigates privacy concerns in low-risk office settings, we do not know if it works for riskier situations that can occur in telecommuting settings. Using a controlled experiment, we evaluated blur filtration for its effectiveness in balancing privacy with awareness for typical home situations faced by telecommuters. Participants viewed five video scenes containing a telecommuter at ten levels of blur, where scenes ranged from little to extreme privacy risk. They then answered awareness and privacy questions about these scenes. Our results show that blur filtration is only able to balance privacy with awareness for mundane home scenes. The implication is that blur filtration by itself does not suffice for privacy protection in video-based telecommuting situations; other privacy-protecting strategies are required.
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) | 0.000 | 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