Bibliographic record
Abstract
Whether, and in what circumstances, public authorities should be held liable for negligence in the performance of their public functions is a highly complex area of the law. Written by Cherie Blair and Dan Squires QC, the first edition of The Negligence Liability of Public Authorities provided a much needed guide to these complexities and offered a detailed account of the law for practitioners and academics.This second edition builds on the reputation of the first, including full coverage of the many important cases which have been decided since 2006. Divided into two parts, Part I focuses on the extent to which the public nature of a defendant affects civil liability and the principles that govern and limit that liability. Part II considers the law as it impacts upon specific areas of public authorities' activities. It examines cases in a range of key areas, including the police, social services, highways, education, and the emergency services and aims to set out in a comprehensive way the different legal issues that have arisen in each area. By examining cases in a variety of jurisdictions, including Australia, Canada, South Africa, New Zealand and the USA, the authors further broaden the scope of this authoritative text. The book also identifies the underlying principles and policy arguments which have shaped the law more generally, making it an extremely useful resource for a wide variety of practitioners.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".