Revisiting the fraud exception: a critique of United City Merchants v Royal Bank of Canada 40 years on
Bibliographic record
Abstract
Abstract Much has changed in the four decades since United City Merchants v Royal Bank of Canada, in which Lord Diplock established the fraud exception in transactions financed by documentary credit. In particular, the introduction of the UCP 600, case law on nullity documents and amendment to the American fraud exception justify a reconsideration of both the policy arguments underpinning Lord Diplock's rule and the fate of documents known to be forged or null at the time of presentation. Accordingly, two arguments are made in this paper. First, a consideration of the broader exception in the US should prompt a modern Supreme Court to re-examine his Lordship's insistence that a narrow exception was required to preserve the efficiency of the credit mechanism. In addition, it further argues that banks should be entitled to reject known nullities and forgeries as non-complying. This argument would reinstate the doctrine of strict compliance, which was overlooked in United City Merchants, and is based on the clarified definitions in the UCP 600, more recent judicial consideration of nullities and the existence of the ICC's International Maritime Bureau.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.024 | 0.045 |
| Scholarly communication | 0.020 | 0.006 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.022 | 0.030 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".