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Deregulation and Participation: An International Survey of Participation in Electricity Regulation

2004· article· en· W2111642914 on OpenAlexaff
Anil Hira, David Huxtable, A. Leger

Bibliographic record

VenueGovernance · 2004
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicRegulation and Compliance Studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsDeregulationStatus quoPublic economicsBusinessVariety (cybernetics)GlobeIndependence (probability theory)Set (abstract data type)EconomicsMarket economy

Abstract

fetched live from OpenAlex

Amidst the wave of privatization and “deregulation” across the globe, a new set of regulatory structures is being created. The fact that deregulation actually involves “re-regulation” has been acknowledged in the recent literature, but the tension between regulation and public participation has been understudied in these new structures. While some private markets need effective regulation to reduce transactions costs and ensure stable market rules, consumers need regulation that is responsive to, and protective of, their interests. Consumer participation, therefore, is an important component of effective regulation. Effective regulation must also consider collective national or public interests, including the well-being of corporations. Therefore, regulatory agencies need to be both independent from, and responsive to corporate, consumer, and public interests. This article will briefly examine the tension among the competing goals of regulatory independence and responsiveness, and then conduct a broad survey of the status quo of public participation in national regulatory structures for electricity in the Americas. Our case studies demonstrate a wide variety of institutional mechanisms for participation, yet we find that no existing system seems to embrace direct participation by a wide set of consumers. The problems are even more acute in developing countries. We conclude by looking at recent experiments and proposals to improve the levels of participation in regulatory decision making.

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 imitation

Not 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.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.013
Science and technology studies0.0010.004
Scholarly communication0.0030.005
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.044
GPT teacher head0.296
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations29
Published2004
Admission routes1
Has abstractyes

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