Bibliographic record
Abstract
The utility regulation framework developed in the UK in the 1980s, and widely adopted internationally, was intended to improve on the restrictive, inefficient and burdensome regulatory approach in the US. But the UK regulatory process has itself now become increasingly burdensome. Meanwhile, utilities and customer groups in the US and Canada have developed methods of negotiating and settling regulatory issues that more directly reflect the interests of customers, often embody incentive price caps as in the UK, and avoid unduly burdensome regulatory processes. There is now scope for UK regulators to learn from overseas. This paper summarises these developments. It then examines how three UK utility regulators— of airports, water and energy—are responding to them by developing new forms of customer engagement. The CAA has moved firmly in this direction for airports, while Ofwat and Ofgem have nominally rejected it for water and energy, but seek to secure many of the benefits of the approach via less committed processes. There is scope for governments to encourage a regulatory approach that offers the prospect of better outcomes for customers and a less onerous process for all concerned. DOI: 00.0000/ISSN2160-5882/E-ISSN2160-5890/EEEP-Vol1-No1-6
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".