PRISM and privacy: will this change everything?
Bibliographic record
Abstract
Both the offline and online media have reported extensively on access by the US National Security Agency (NSA) to electronic communications data held by private companies, most notably via the so-called PRISM program. Meanwhile, there is growing concern regarding reports the UK's Government Communications Headquarters (GCHQ) is conducting massive surveillance of communications traffic both on its own behalf and for the benefit of other members of the ‘Five Eyes Alliance’ (comprising the UK, the USA, Canada, Australia, and New Zealand), and other European governments have been reported to have entered into arrangements to share the data collected by the USA and the UK. At least one European government (France) allegedly also runs a vast electronic surveillance operation of its own. We hesitate to make pronouncements about such developments before the facts are clear, but feel justified in predicting that they will have significant long-term impacts on data protection and privacy law around the world, and on the political, economic, and social climate for data processing.
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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 | 0.037 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.058 |
| Scholarly communication | 0.027 | 0.056 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.024 | 0.047 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".