Clearing clouds of uncertainty—a principled approach to determining what qualifies as an ‘accident’ under the Montreal Convention
Bibliographic record
Abstract
Abstract The revival of international air travel in the wake of the receding COVID-19 pandemic has refocused the spotlight on the inherent hazards of carriage by air. From a legal perspective, the focal issue is whether any of those air travel-related incidents that may result in passenger death or bodily injury are compensable accidents. This article examines the meaning and scope of the term ‘accident’ under Article 17(1) of the 1999 Montreal Convention (MC99)—the exclusive regime for air carrier liability within international air transportation. The term ‘accident’ is a condition precedent to air carrier liability but has been left undefined by the drafters of MC99, leaving it to national courts to interpret its meaning. Accordingly, the article utilizes a doctrinal and normative methodology to review a select body of case law drawn primarily from common law jurisdictions. It proceeds to identify a set of ten principles that would guide future courts to ascertain whether an alleged event qualifies as an ‘accident’ consistently and predictably. The article also discusses the controversial interpretation of ‘accident’ by the Court of Justice of the European Union, signaling judicial discord, and sheds light on the consequent implications, especially in regard to MC99’s pursuit of global uniformity and achieving a fair balance of interests between passengers and air carriers.
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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 | 0.071 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.009 | 0.071 |
| Scholarly communication | 0.022 | 0.016 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 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".