Nareerux Redux: The Ontario Court of Appeal Fashions Novel Letter of Credit Law
Bibliographic record
Abstract
This paper discusses issues raised by what one must hope is the now infamous case of Nareerux Import Co. v. Canadian Imperial Bank of Commerce, which the Ontario Court of Appeal has now affirmed in large part. This commentator’s criticism of the Ontario Superior Court’s judgment appeared at 24 Banking & Finance Law Review 551 (2009), and this paper has been submitted to the same journal for publication. Happily, the Ontario Court of Appeal reversed the lower court’s obvious error in failing to apply the ICC’s Uniform Customs and Practice for Documentary Credits to credits that incorporated the Uniform Customs explicitly; but, sadly, the Court of Appeal, followed the lower court’s lead in adding good faith observance and fiduciary duties to the issuer’s duties under the credit. The facts disclose that the beneficiary of the credits that enjoys the more than $10 million dollar benefit of these decidedly un-commercial judgments had engaged in the most supine commercial behavior. Yet, moved by concerns that reflect gross lack of appreciation for the realities of commercial letter of credit transactions, the courts have fashioned letter of credit law for Ontario that may raise the cost of letters in that important Canadian province with no discernible benefit to commercial parties.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.008 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.012 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".