Geographically segmented regulation for telecommunications: lessons from experience
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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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 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".