The Legal Approach to Investor Liability Conditions for Damage to Third Parties on the Ground within National Scope in the UAE Civil Aviation Law No. 20 of 1991 and the Saudi Civil Aviation Law No. 44 of 1426 AH: A Comparative Study
Bibliographic record
Abstract
The damage caused by non-military aircraft to persons or property on the ground has long been a subject of international concern. Two key conventions were established to regulate liability arising from such damage: the 1952 Rome Convention, commonly known as the Rome Convention on “Damage Caused by Foreign Aircraft to Third Parties on the Surface,” and, later, the Montreal Convention of 2009, formally known as the “Convention on Compensation for Damage Caused by Aircraft to Third Parties.” The Montreal Convention was a result of the signatory states to the Rome Convention recognizing the need to review and update certain provisions. Both the United Arab Emirates and the Kingdom of Saudi Arabia are signatories to this convention. While international regulations play a significant role, domestic legislation is equally critical. It is therefore incumbent upon nations to enact clear legal provisions to regulate the liability of aviation investors for damage caused by aircraft to third parties on the ground, from a national legal perspective. This study addresses an issue of critical importance, namely the need for precise regulations that clarify the conditions of an aviation investor’s liability for damage to third parties on the ground within the national legal frameworks of the UAE and Saudi Arabia. Through comparative analysis, this research seeks to address the legislative gaps found in the UAE’s Civil Aviation Law No. 20 of 1991 on this matter.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".