The law of freedom of information
Bibliographic record
Abstract
A INTRODUCTION TO THE FREEDOM OF INFORMATION ACT 2000 1. Which set of rules? 2. An Overview 3. The historical background B. THE STATUTORY SCHEME 4. Access to information held by public authorities 5. Exempt information 6. Enforcement 7. Historical records 8. The position of Local Authorities C. ENVIRONMENTAL INFORMATION 9. The Environmental Regulations 2004 D. PERSONAL INFORMATION 10. Data Protection E. EUROPE 11. Freedom of Information in the European Union F. FREEDOM OF INFORMATION AND THE COMMERCIAL WORLD 12. Implications for business 13. Freedom of Information in commercial disputes 14. The internet 15. Freedom of Information and intellectual property G. PRIVACY, CONFIDENTIALITY AND HUMAN RIGHTS 16. Privacy and confidentiality 17. The Human Rights Act 1998 H. RELATED STATUTES 18. Secrecy 19. Whistleblowing 20. Money laundering 21. Medical records 22. Land registration Acts, the Registration Acts and the Census Acts I. DEVOLUTION 23. Scotland 24. Wales and Northern Ireland J. A COMPARATIVE VIEW 25. The Right to know in Australia, Canada, France, Ireland, New Zealand, and the USA K. THE FUTURE 26. The Future APPENDICES
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.024 | 0.012 |
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".