Transportation and unmanned technology: fiction or the future? a first glance on the legal issues arising from the use of unmanned vehicles for transport purposes
Bibliographic record
Abstract
Within the subject of unmanned aircraft, many aspects have been addressed by scholars and institutions, for instance concerning third party liability, insurance, security and privacy. However, one of them has been hitherto particularly neglected, although occupying a position of remarkable importance m this branch of law: That is transport. It is evident that such hesitation in making a connection between unmanned aircraft and carriage of persons or goods is certainly not due to a deficiency of the technological evolution. On the contrary, the high level of automation already achieved in flight operations proves the total reliability on technology. Moreover, the shipping sector has started such investigation on carnage of goods in the earliest stage of its unmanned history Thus, what are the specific issues which should be addressed by the legal sciences? A parallel look at the existing rules m the field of maritime transport, e.g. the Hague-Visby Rules, shows that here the manning component finds an express mention, making a legal intervention necessary for the introduction of such technology. The Montreal Convention, instead, contains no explicit obligation on the carrier to man the aircraft. On the contrary, the liability regime created by such Convention, for instance the one on damage to cargo under Article 18, does not seem inconsistent with such innovation on aircraft technology. However, many other instruments at all levels obviously provide rules implying the presence of a crew on board. This paper is an attempt to give first insights on the topic, likely to be m the center of the legal reflection in the coming years.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".