Systematic Government Access to Private-Sector Data Redux
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.039 | 0.141 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".