Bibliographic record
Abstract
In June 2013, Edward Snowden revealed a secret US government program that collected records on every phone call made in the country. Further disclosures followed, detailing mass surveillance by the UK as well. Journalists and policymakers soon began discussing large-scale programs in other countries. Over two years before the Snowden leaks began, Cate and Dempsey had started researching systematic collection. Leading an initiative sponsored by The Privacy Projects, they commissioned a series of country reports, asking national experts to uncover what they could about government demands that telecommunications providers and other private-sector companies disclose information about their customers in bulk. Their initial research found disturbing indications of systematic access in countries around the world. These programs, often undertaken in the name of national security, were cloaked in secrecy and largely immune from oversight, posing serious threats to personal privacy. After the Snowden leaks, the project morphed into something more ambitious: an effort to explore what should be the rules for government access to data and how companies should respond to those demands within the framework of corporate responsibility. This volume concludes the nearly six-year project. It assembles 12 country reports, updated to reflect recent developments. One chapter presents both descriptive and normative frameworks for analyzing national surveillance laws. Others examine international law, human rights law, and oversight mechanisms. Still others explore the concept of accountability and the role of encryption in shaping the surveillance debate. In their conclusion, Cate and Dempsey offer recommendations for both government and industry.
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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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.279 | 0.191 |
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".