Managing the Skies: Public Policy, Organization and Financing of Air Traffic Management
Bibliographic record
Abstract
Over the past two decades, the organization and provision of air traffic control (ATC) services has been dramatically transformed. Privatization and commercialization of air navigation has become commonplace. Far-reaching reforms, under a variety of organizational structures and aviation settings, have occurred across the world, most notably in Canada, Britain, Australia, New Zealand, and South Africa. In contrast, innovations have lagged behind in other countries - including the United States. In addition, much recent attention has been given to aviation infrastructure and safety in Africa, in some parts of Asia and Latin America, and in rapidly growing air markets including India and China. In response, the International Civil Aviation Organization (ICAO), the International Air Transport Association (IATA), and multilateral banks and institutions have launched a major effort to improve the performance and safety of civil aviation in developing economies. Managing the Skies has been written to provide a guide to what has been tried in air traffic management, what has worked, and what lessons might be learned. The book starts with an introduction to air navigation, its development and current state, as well as trends in aviation activity. It examines in detail the experiences of ATC in both mature and emerging markets across the world, considering many alternative models, efforts to restructure and comparisons of performance. The book contains several in-depth case studies to provide a truly global perspective of ATC practices. Particular attention is given to the FAA and its efforts and challenges in reforming ATC in the US, both historically and in the current climate. It addresses the issues of finance, organization, investment, and safety restructuring and reform options that are at the core of current debates involving air traffic control in the United States. Further to this, the authors discuss the alternatives available for future change. The book concludes by examining the cross-cutting issues of labor relations and organizational structures, presenting the lessons learned and considering what the future may hold. As the world experiences a resurgence in air travel and civil aviation, the issues discussed in Managing the Skies are particularly timely not only for industry and government leaders, but for the world's air travelers.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".