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Atiyah’s Introduction To The Law Of Contract

2006· book· en· W612390946 on OpenAlexaff
P. S. Atiyah, Stephen A. Smith

Bibliographic record

Venuenot available
Typebook
Languageen
FieldSocial Sciences
TopicLegal principles and applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsLawExposition (narrative)Context (archaeology)Comparative lawLegislationExclusion clauseCommercial lawPrivate lawPolitical scienceSociologyContract managementEconomicsManagementHistory

Abstract

fetched live from OpenAlex

Abstract Atiyah's Introduction to the Law of Contract is a well-known text through which thousands of university students have first encountered the law of contract, and the new edition has long been eagerly awaited by university teachers and students. This sixth edition, updated by Stephen Smith, continues to provide readers with an introduction to the theories, policies, and ideas that underlie the law, placing an equal emphasis on the law and critical analysis. In particular, the discussion of recent cases and legislation is centred on why contract law is the way it is, whether it can be justified, and, if not, what should be done to improve it. The sixth edition has been revised to place the law of contract in a modern context and to account for recent developments in the law, as well as those in academic thinking and writing. Addressing European influences and including perspectives from comparative law, this remains a stimulating and authoritative exposition of the modern law of contract.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.030
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0030.006
Scholarly communication0.0050.005
Open science0.0010.002
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0300.007

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.

Opus teacher head0.016
GPT teacher head0.292
Teacher spread0.276 · 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

Citations371
Published2006
Admission routes1
Has abstractyes

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