Bibliographic record
Abstract
Most airports have an elevated vulnerability to aviation market fluctuations, which was emphasised during the coronavirus disease-2019 pandemic, when most airports experienced traffic downturns in excess of 90 per cent. This led them to batten the hatches by cutting operational costs to the bone, shelving major capital programmes and shuttering swathes of terminal infrastructure. Worldwide, terminals became ghost towns, and there were rumours of airport bankruptcy. The crisis made it patently clear that airports typically had very little alternative income with which to keep the wolf from the door during such events, leading the authors to ask the question whether the airport business is not too specialised and whether it might not benefit from diversification. In considering this question, it became apparent that the airport business as we know it today might also be in danger of major disruption within the next decade. This paper argues that it might be time for airports to reassess their business model by asking the question: What business are we really in? The proposed answer might be surprising for many airport authorities who have focused on aviation as their core business for the past century. The paper offers a range of provocative thoughts and ideas aimed at encouraging airport authorities to reassess their strategic plans and innovate towards a more resilient and sustainable business model that is integrated with their surrounding communities and regions, while staying ahead of the evolution of the mobility market.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".