Two perspectives on trading in radio spectrum usage rights: Coase and Commons compared
Bibliographic record
Abstract
Abstract In this contribution, we address the introduction of private property rights and market trades in the use of the radio frequency spectrum. We discuss the UK case being inspired by the ideas of Coase. We discuss how an appropriate design of property rights and a secondary market would look like and how the developments after the introduction of property rights could be interpreted. Subsequently we present the alternative perspective of Commons to illuminate the implications of a Coasean perspective. It is shown how Coase's focus is on efficiency, whereas in the world of Commons, the societal value is central. We discuss how the two perspectives can contribute to the understanding of the governance of the radio spectrum and conclude with policy recommendations.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.037 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".