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Record W584355607

Dualism, Science, and the Law: The Treatment of the Mind-Body Dichotomy under Article 17 of the Montreal Convention

2009· article· en· W584355607 on OpenAlexaboutno aff
Hanna Chouset

Bibliographic record

VenueIssues in aviation law and policy · 2009
Typearticle
Languageen
FieldNeuroscience
TopicFree Will and Agency
Canadian institutionsnot available
Fundersnot available
KeywordsLawConventionDamagesTreatyDualismSupreme courtPolitical scienceInternational lawSociologyPhilosophyEpistemology
DOInot available

Abstract

fetched live from OpenAlex

The liability of air carriers in international transportation is governed by the Montreal Convention. The Convention treaty states that the carrier is liable for damages arising out of bodily injury. However, judicial opinion has ruled that the carrier is not liable for damages arising from emotional injury, unless that emotional injury is causally related to a bodily injury for which the carrier is liable. The article surveys the development of the law under Article 17 of the Warsaw Convention (which preceded the Montreal Convention) as expounded by the U.S. Supreme Court and explores the concept of dualism and its underling presence in Article 17 litigation, as well as some of the popular philosophical counterarguments to dualism. Recent medical developments and court cases regarding neuroimaging and post-traumatic stress disorder are discussed. The Montreal Convention is discussed, including the intent of the signatories to continue to limit Article 17 as it was developed under the Warsaw Convention. This article argues that, despite challenges to the mind-body dichotomy, the courts have correctly rejected the argument that emotional damages are compensable under the Montreal Convention. The author concludes that the current state of the law under Article 17 strikes the correct balance between preventing a flood of emotional injury claims while providing appropriate compensation to victims of aviation accidents.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.741
Threshold uncertainty score0.585

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.303
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2009
Admission routes1
Has abstractyes

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