The Public to Private Continuum Measure and the Role of Stakeholder Boards as a Proxy for Markets in the Governance of Air Navigation Services: A Comparative Analysis
Bibliographic record
Abstract
This article studies institutional arrangements for governance of air navigation services employing a comparative analysis of six nations: Australia, Canada, New Zealand, Switzerland, the United Kingdom, and the United States. In each of these countries other than the United States, a board of directors composed of stakeholder representatives manages an independent air navigation services organization that is not a traditional government bureau. In this article we assess how boards of public organizations can act as a proxy for market feedback in the provision of public services. We use this concept to develop a more sensitive measure of the degree of publicness and privateness in organizations. We test our Public-Private Continuum Measure using a comparative analysis of air navigation services in six countries. Our Public-Private Continuum Measure advances the measurement of the public to private continuum from the use of an ordinal measure to a continuous measure. Further research is needed to test this measure in studies that place organizations on the continuum and determine how the degree of public-privateness correlates with organizational performance measures. Armed with this tool, governments can make more accurate decisions about the degree of public-privateness desired for the provision of public goods.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".