Bibliographic record
Abstract
The most plausible means for systematic UK government access to private-sector data is through voluntary agreements with the operators of systems and databases. This was how Internet Service Providers' communications records were accessed by police before specific statutory provision was made in the Regulation of Investigatory Powers Act 2000 (RIPA). Sections 28–29 of the Data Protection Act 1998 allow such voluntary arrangements for purposes related to national security, law enforcement, and taxation. Companies such as Facebook and RIM/BlackBerry have publicly acknowledged that they provide access to specific user data when UK public authorities follow the RIPA procedures, even though they are not legally required to.1 UK ISPs must retain records about their customers' Internet sessions and e-mail, although not message contents, under the Data Retention Regulations 2009. The government continues to discuss new legal powers that would require ISPs to store records relating to their customers' communications on webmail, social media, and other sites, which could then be accessed on a semi-automated but particularized basis under RIPA. It is likely that for national security purposes the government's signals intelligence agency, GCHQ, undertakes large-scale surveillance of Internet data transfers to or from points outside the UK. This can be authorized under RIPA, and telecommunications providers required to facilitate interception under that Act and the Telecommunications Act 1984. Under the UKUSA2 agreement GCHQ cooperates extremely closely with intelligence agencies in the USA, Canada, Australia, and New Zealand. It is likely that any access these agencies have to private-sector data will be shared to some extent. However, such activities are highly secret.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.007 |
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".