Corporate management and executive liability in the airport industry
Bibliographic record
Abstract
In the past, an airport was simply a terminus, much like a bus terminus of that time, assigning it as the focal geographical point at which people gathered to embark on a plane for a journey by air, or disembark after an air journey. However, the traditional definition of an airport is being reshaped and refined to accord with the fact that airports are now complex industrial enterprises. Quite apart from the essential air-side support given by airports to landing and departing aircraft, there are commercial facilities provided for both passengers and the public within the terminal building by concessionaires who are specialists in their own fields of business. More and more, airports are evolving from being basic aeronautical infrastructures into complex, multi-functional enterprises serving the travelling public while at the same time catering to their commercial needs and those of others who visit the airport. Such enterprises include duty-free shops, speciality retail and brand-name shops, restaurants, hotels and accommodation, banks, business and office complexes, leisure, recreation and fitness centres, to name just a few. Current commercial activities at an airport bring to bear the relevance of business law in the day-to-day running of the airport. The most significant of legal elements in this context lie in the area of corporate negligence and liability. This paper addresses the areas of corporate management, corporate and executive liability and negligent entrustment.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| 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".