Geographically segmented regulation for telecommunications: lessons from experience
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The aim of this paper is to make policy makers and regulators more fully aware of the practical problems and costs involved in implementing geographically segmented regulation. This awareness will be valuable in deciding whether to adopt the approach and, if so, in designing its implementation, i.e. how the scheme's problems will be addressed and costs minimized. Design/methodology/approach Increasingly, incumbent operators and some regulators have argued that regulatory forbearance should be adopted in geographic areas (usually the more densely populated cities) where facility‐based competition is developing. Certainly geographically segmented regulation accords with widespread agreement that regulation should be the minimum necessary. Indeed, a number of countries have implemented the scheme, including Australia, Austria, Canada, Finland, Portugal, Spain, the UK and USA. This paper examines the experience these countries have had in applying geographically segmented regulation. Findings The lessons from experience in applying geographically segmented regulation suggest that the processes used to determine specific relevant markets are, at present, contentious and problematic in principle, and complex and subjective in practice. The problems/costs relating to the implementation of geographic regulation could erode the stability, certainty and predictability so important in a regulatory regime. Moreover, outcomes are uncertain, especially when looking ahead into an NGN environment. Originality/value This is the first paper that examines the actual experience of countries that have implemented geographically segmented regulation.
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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 | 0.000 | 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) | 0.000 | 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 it