Bibliographic record
Abstract
This chapter focuses on the regulation of Air Traffic Control (ATC): how it is done, and how it can be improved. The analysis is general, but it pays specific attention to Europe, which is a region which is dominated by public but regulated suppliers. Major parts of ATC systems, especially the en-route systems, are characterised by natural monopoly. Worldwide, most ATC systems are publicly owned and operated, though there are significant exceptions, such as those of the United Kingdom and Canada. In the EU ATC systems are government owned, though many are corporatised. With ATC systems there is a short run problem of achieving productivity, and in some cases, there are also quality problems, which are manifested in delays. In the long run there are problems of achieving efficient levels of investment. In the EU there is strong evidence of productive inefficiency in many countries’ systems, and pricing often involves traffic risk sharing mechanisms, which dampen incentives for efficiency. Questionable incentives for efficiency in government owned systems are an issue. Turning to the long run, in Europe there have been chronic problems of achieving adequate investment, made more complex by the difficulties in achieving interoperability between the systems of different countries. The EU system of regulation is such that incumbent operators are shielded from risks (very evident in the Covid crisis), leading to weak incentives for efficiency.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.010 |
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".