Contract Negotiations and the Common Law: A Move to Good Faith in Commercial Contracting?
Bibliographic record
Abstract
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 Hrynew2014 SCC 71 andCallow v Zollinger2020 SCC 45 in Canada. This paper will examine the extent to which these cases may open the way more generally for a duty tonegotiatecommercial 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.137 |
| Scholarly communication | 0.028 | 0.030 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.015 | 0.022 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".