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Record W626267911 · doi:10.69554/mjiv4757

Corporate management and executive liability in the airport industry

2010· article· en· W626267911 on OpenAlexaff
Ruwantissa Abeyratne

Bibliographic record

VenueJournal of airport management · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Law and Aviation
Canadian institutionsInternational Civil Aviation Organization
Fundersnot available
KeywordsBusinessLiabilityExecutive summaryAccountingFinance

Abstract

fetched live from OpenAlex

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.

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)0.003
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.763
Threshold uncertainty score0.236

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.022
GPT teacher head0.293
Teacher spread0.271 · 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 designObservational
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

Citations0
Published2010
Admission routes1
Has abstractyes

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