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Record W4288685648 · doi:10.1007/s10991-022-09299-2

Contract Negotiations and the Common Law: A Move to Good Faith in Commercial Contracting?

2022· article· en· W4288685648 on OpenAlexaboutno aff
Paula Giliker

Bibliographic record

VenueLiverpool Law Review · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicLegal principles and applications
Canadian institutionsnot available
Fundersnot available
KeywordsLawGood faithNegotiationCommon lawDutyPolitical scienceFaithFair dealingBad faithConventionTheology

Abstract

fetched live from OpenAlex

Abstract Classically a duty to negotiate commercial contracts in good faith has been seen as part of the civil, not the common, law world. Common law commercial lawyers have long resisted the lure of “good faith” as a contractual concept, despite engagement with civil law principles in harmonisation projects, by virtue of membership of the European Union and their use in international conventions such as the United Nations Convention on Contracts for the International Sale of Goods (CISG). This paper will examine whether this situation is changing, focusing on two common law jurisdictions—England and Wales and Canada. In England and Wales and the common law of Canada, case-law in the last 10 years has indicated a movement towards acceptance of express and implied duties of good faith in relation to contractual performance, see e.g. Yam Seng Pte Limited v International Trade Corporation Limited [2013] EWHC 111 (QB) and, most recently, Essex CC v UBB Waste (Essex) Ltd (No. 2 ) [2020] EWHC 1581 (TCC) in England and Wales; Bhasin v Hrynew 2014 SCC 71 and Callow v Zollinger 2020 SCC 45 in Canada. This paper will examine the extent to which these cases may open the way more generally for a duty to negotiate commercial contracts in good faith. It will examine the reception of these cases and whether they indicate (i) greater acceptance of “good faith” as part of contract law thinking and (ii) a possible extension of good faith into the pre-contractual period.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.993
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.022
GPT teacher head0.324
Teacher spread0.302 · 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 designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations6
Published2022
Admission routes1
Has abstractyes

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