Bibliographic record
Abstract
Rapid developments in the aviation industry bring to bear the compelling need to reexamine algorithms in air law. For example, the advent of advanced air mobility (AAM), typified by eVTOL aircraft and algorithm-driven systems, compels an evaluation of traditional air law. Rooted in treaties such as the Paris Convention of 1919 and the Chicago Convention of 1944, air law has evolved incrementally in response to the growth of global aviation. However, the rapid emergence of technologies such as autonomous aircraft, drones, and quantum computing necessitates a transformative approach. Algorithms, once peripheral to legal considerations, now lie at the heart of this evolution. These systems provide not only a means of optimizing safety and efficiency but also an avenue for addressing the intricate interplay of liability, governance, and ethical considerations. The Council of the International Civil Aviation Organization (ICAO) stands at the forefront of this transformation. Leveraging its role as a global standard-setter, the Council can convene stakeholders to develop adaptive legal instruments, emphasizing cybersecurity protocols, liability apportionment, and equitable access. By fostering interdisciplinary collaboration and engaging in proactive governance, ICAO can ensure that AAM integrates innovation with fairness and resilience. Ultimately, the integration of algorithms into air law represents more than a technological shift; it demands a philosophical reorientation. The algorithm emerges not only as a tool but also as a metaphor for interconnectedness and adaptability. Air law, in embracing this paradigm, must transcend prescriptive rules to become a living, dynamic framework capable of guiding aviation into an equitable, sustainable future. Only through such an approach can we ensure that the skies remain navigable, secure, and just, reflecting a balance between technological progress and human values. This article examines the issues involved.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".