Systematic Government Access to Private-Sector Data Redux
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 November 2012 we published a symposium issue (volume 2, number 4 of IDPL1) containing a series of papers analysing the laws and practices of nine countries (Australia, Canada, China, Germany, India, Israel, Japan, the UK, and the USA) relating to systematic government access to personal data held by the private sector. Those papers, developed as part of a multi-year project funded by The Privacy Projects—a not-for-profit organization dedicated to improving current privacy policies, practices and technologies through research, collaboration, and education2—demonstrated considerable consistency in the laws and practices of the nine countries examined. According to a guest editorial that accompanied the papers, common trends included: ... Although published more than a year ago, those papers proved remarkably prescient in light of the subsequent disclosures by Edward Snowden and others during the past year about sweeping surveillance programmes in the United States and the United Kingdom. The programmes disclosed seemed to bear out the common themes previously identified, especially about the intensity of government demands for private-sector data, the lack of transparency about the surveillance, and the wide chasm between what the laws (and governments) say and what really takes place.
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 | 0.003 | 0.008 |
| 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 | 0.001 | 0.004 |
| Open science | 0.017 | 0.014 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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