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Record W2515536272 · doi:10.5547/01956574.38.3.tjam

A Quarter Century Effort Yet to Come of Age: A Survey of Electricity Sector Reform in Developing Countries

2016· article· en· W2515536272 on OpenAlexaboutno aff
Tooraj Jamasb, Rabindra Nepal, Govinda R. Tmilsina

Bibliographic record

VenueThe Energy Journal · 2016
Typearticle
Languageen
FieldEngineering
TopicElectric Power System Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsElectricityEquity (law)EconomicsDeveloping countryPovertyBusinessEconomic reformPublic economicsEnergy povertyQuarter (Canadian coin)Economic policyEconomic growthDevelopment economicsPolitical sciencePolitics

Abstract

fetched live from OpenAlex

More than two decades have passed since the start of the worldwide market- oriented electricity sector reforms. The reforms have varied in terms of structure, market mechanisms, and regulation. However, the passage of time calls for taking stock of the performance of the reforms in developing countries. This paper surveys the empirical literature on electricity sector reforms and draws some conclusions with a view to the future. Overall, the reforms have tended to improve the technical efficiency of the sector. The macroeconomic benefits of reforms are less clear and remain difficult to identify. Also, the gains from the reforms have often not trickled down to consumers because of institutional and regulatory weaknesses. In order to achieve lasting benefits, reforms need to adopt measures that align their pursuit of economic efficiency with those of equity and provision of access. Reforms can deliver more economic benefits and alleviate poverty when the poor have access to electricity. New technologies and institutional capacity building can help improve the performance of reforms.

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)0.002
metaresearch head score (Gemma)0.007
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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.009
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0000.001
Research integrity0.0010.001
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 head0.009
GPT teacher head0.207
Teacher spread0.198 · 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

Citations95
Published2016
Admission routes1
Has abstractyes

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