Air Traffic Control: Preliminary Observations on Commercialized Air Navigation Service Providers
Bibliographic record
Abstract
Testimony issued by the Government Accountability Office with an abstract that begins "In the past, governments worldwide owned, operated, and regulated air navigation services, viewing air traffic control as a governmental function. But as nations faced increasing financial strains, many governments decided to shift the responsibility to an independent air navigation service provider (ANSP) that operates along commercial lines. As of March 2005, 38 nations worldwide had commercialized their air navigation services, fundamentally shifting the operational and financial responsibility for providing these services from the national government to an independent commercial authority. GAO selected five ANSPs--in Australia, Canada, Germany, New Zealand, and the United Kingdom--to examine characteristics and experiences of commercialized air navigation services. These ANSPs used different ownership structures and varied in terms of their size, amount of air traffic handled, and complexity of their airspace. This testimony, which is based on ongoing work, addresses the following questions: (1) What are common characteristics of commercialized ANSPs? (2) What do available data show about how the safety, cost, and efficiency of air navigation services have changed since commercialization? (3) What are some initial observations that can be made about the commercialization of air navigation services?"
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 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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| 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".