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Record W1492898583 · doi:10.1177/178359171201300104

Liberalization Models in the Electricity Sector: The Impact of the Market for Corporate Control in Britain and Spain (1996–2010)

2012· article· en· W1492898583 on OpenAlexaff
Rocío Valdivielso del Real, Michel Goyer

Bibliographic record

VenueCompetition and Regulation in Network Industries · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsInstitute on Governance
Fundersnot available
KeywordsRestructuringLiberalizationCorporate governanceElectricityMarket economyElectricity marketBusinessArgument (complex analysis)Independence (probability theory)EconomicsElectricity retailingEconomyAccountingFinance

Abstract

fetched live from OpenAlex

Liberalization and re-regulation have brought along major changes in the regulated environment of utility industries, and particularly in the electricity sector. Nonetheless, the impact of these developments exhibits substantial differences. This article analyses the diverging impact of the market for corporate control (i.e. takeovers) on the evolution of the electricity sector in Britain and Spain. Takeovers have been prominent in the restructuring of the electricity sector in Britain. Every single privatised company has been acquired by foreign rivals in a short span of time. The Spanish electricity sector, in contrast, has been unequally exposed to takeovers: changes in ownership at Iberdrola and Union Fenosa were characterised by a friendly transition while Endesa has been acquired by Enel despite the preferences of Spanish policy-makers for the building of national champions. The argument presented in this article stresses the continuing importance of three key national institutions of corporate governance –ownership structures of listed companies; the system of corporate law and its associated voting rights procedures; and the degree of independence of regulatory authorities.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.420
Threshold uncertainty score0.331

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.040
GPT teacher head0.270
Teacher spread0.230 · 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 teacher head, 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

Citations3
Published2012
Admission routes1
Has abstractyes

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