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The Law of Rescission

2023· book· en· W4401782410 on OpenAlexaboutno aff
Dominic O'Sullivan KC, Steven Elliott KC, Rafal Zakrzewski

Bibliographic record

VenueOxford University Press eBooks · 2023
Typebook
Languageen
FieldSocial Sciences
TopicLegal principles and applications
Canadian institutionsnot available
Fundersnot available
KeywordsRescissionLawPolitical science

Abstract

fetched live from OpenAlex

Abstract This book, the leading work in the field, is an extensive analysis of the law concerning the rescission of contracts and gifts in England and Wales, and also contains detailed reference to the law of other parts of the Commonwealth including Australia, New Zealand, Canada, British Virgin Islands, Cayman Islands, Isle of Man, Singapore, Hong Kong, Malaysia, and India. The revised third edition builds on the established format, fully updating case law and considering how developments, such as the introduction of machine-generated contracts, impacts on the law. The book also incorporates new legislation, such as The Insurance Act 2015. Rescission is frequently sought in commercial, property, and insurance disputes, making this book an essential reference for all lawyers involved in civil litigation, as well as for civil judges. Academics will also find this book of interest when researching contracts, remedies, or restitution.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.020
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0050.016
Scholarly communication0.0090.005
Open science0.0020.004
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0200.005

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 head0.041
GPT teacher head0.271
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations4
Published2023
Admission routes1
Has abstractyes

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