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Misrepresentation

2006· book-chapter· en· W4388416635 on OpenAlexaff
P. S. Atiyah, Stephen A. Smith

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicEuropean and International Contract Law
Canadian institutionsMcGill University
Fundersnot available
KeywordsMisrepresentationDamagesLaw and economicsAsideTortNegotiationBusinessLawPolitical scienceEconomicsLiabilityPhilosophy

Abstract

fetched live from OpenAlex

Abstract While there is no general rule allowing for a contract to be set aside should one party fail to disclose material facts during negotiations, it is a strict and universal rule that such a right exists where one party is induced to enter a contract on the basis of the other’s false representation. In principle, such relief may be available even where the ‘representor’ was not at fault and even if the misrepresentation concerned a trivial aspect of the contract. Moreover, a person who has been induced to enter a contract by a misrepresentation will often have the additional right to claim damages in tort for any loss caused by the misrepresentation.

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)0.004
metaresearch head score (Gemma)0.009
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 score0.013
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0020.009
Scholarly communication0.0060.007
Open science0.0020.004
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0130.003

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 head0.037
GPT teacher head0.313
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

Citations0
Published2006
Admission routes1
Has abstractyes

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