Winter blues at European airports: The need for airport responsibility and corporate foresight
Bibliographic record
Abstract
The chaos at European airports, particularly London Heathrow, in the winter of 2010, wrought by pummelling snowstorms and ice, not only caused cancelled flights and stranded passengers but also resulted in a tremendous cost for the airport authorities. Airports operator BAA has released the figure of £24m (c. US$38m) as the cost incurred by the Spanish-owned firm which operates six airports in Britain. This cost includes a reduction in profit as a result of the decrease in passenger numbers by 11 per cent over a few days. As this paper discusses, the handling of the crisis by BAA drew widespread criticism from both legislators and airlines, the former considering the adoption of legislation against airports and the latter threatening to withhold charges due to the airports. It also asks whether airports should be held responsible for service failure brought about by a natural phenomenon and whether airports should have had what in modern business parlance is called ‘corporate foresight’ to deal with the natural phenomenon. This leads to a discussion on the kind of foresight an airport should have to deal with such situations as well as the obligations of a state to provide functional airport services in its territory. In conclusion, the paper posits that, as regards corporate foresight, an airport has to start with a culture of corporate foresight and adopt a dynamic and comprehensive emergency management process. More importantly, it recommends that airports work jointly and in partnership with airlines and air navigation service providers in developing their corporate foresight.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".