Navigating Through Cloudy Skies: The Montreal Convention & Article 17 “Accidents” Post-Moore
Bibliographic record
Abstract
The Montreal Convention is a multilateral treaty that comprehensively regulates international air carriers. Specifically, Article 17 of the treaty allows passengers to recover against air carriers for injuries or deaths on international flights, so long as certain requirements are met. In Air France v. Saks, the Supreme Court held that “accident”—a controlling term in Article 17—describes an event that is external to the passenger and “unexpected or unusual.” Last year, in Moore v. British Airways PLC, the First Circuit purported to identify a split over what this language means. According to Moore, there are courts who (correctly) gauge whether an event is unexpected or unusual from the perspective of an airline passenger with ordinary experience in commercial air travel (an objective standard that I will call the Reasonable Passenger Approach). On the other hand, there are courts who (incorrectly) determine whether an event is unexpected or unusual from the perspective of the airline industry, using industry norms and customs as guides (a standard I will call the Industry Approach). In this Comment, I will explain why the standard endorsed by the First Circuit in Moore muddies the waters and how a more holistic, totality-of-the-circumstances approach (the Holistic Approach) offers a better rubric for Article 17 “accident” analyses. Given the recency of the Moore case, my goal in this Comment is to provide timely insight on its blind spots while offering an approach that might rescue Moore from a legacy of confusion.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.022 | 0.011 |
| Insufficient payload (model declined to judge) | 0.016 | 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".