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
In the 10 years since IEEE Security & Privacy's initial launch, privacy has moved from being a side story occasionally covered in the newspaper to a central issue of our times. With the Internet, through the rise of online social networks, tracking technologies such as cookies and Web beacons, and the sharing of data with third parties, and the government's increasing use of surveillance mechanisms such as closed-circuit television, wiretapping, and location tracking, almost everyone experiences far less privacy than they did just a decade ago. But at the same time, governments and industry are taking much more of an interest in privacy protection than they did when IEEE Security & Privacy first appeared, particularly because consumers adopt personalized services in large numbers. In this roundtable, five privacy leaders discuss some recent concerns: Ann Cavoukian, Ontario's privacy commissioner, Alan Davidson, a recent head of Google's US public policy office, Ed Felten, who recently served a term as chief technologist at the US Federal Trade Commission, Marit Hansen, deputy privacy and information commissioner of Land Schleswig-Holstein, Germany, and Anna Slomovic, chief privacy officer at Equifax.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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