MétaCan
Menu
Back to cohort

The Negligence Liability of Public Authorities, Second Edition

2019· book· en· W3148539756 on OpenAlexaboutno aff
Duncan Fairgrieve, Dan Squires

Bibliographic record

Venuenot available
Typebook
Languageen
FieldSocial Sciences
TopicLegal Issues in South Africa
Canadian institutionsnot available
Fundersnot available
KeywordsLiabilityVariety (cybernetics)Scope (computer science)Political scienceReputationLegal liabilityLawTortBusiness

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.683
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.040
GPT teacher head0.309
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations13
Published2019
Admission routes1
Has abstractyes

Explore more

Same topicLegal Issues in South AfricaFrench-language works237,207