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Record W2606670653 · doi:10.1515/mgr-2017-0006

Review Essay. Energy landscape research – Lessons from Southern Europe?

2017· article· en· W2606670653 on OpenAlexaff
Bryn Greer‐Wootten

Bibliographic record

VenueMoravian Geographical Reports · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Acceptance of Renewable Energy
Canadian institutionsYork University
Fundersnot available
KeywordsWritPublicationAgency (philosophy)Renewable energyPolitical scienceValue (mathematics)Regional scienceGeographyLibrary scienceEconomySocial scienceSociologyEngineeringLawEconomics

Abstract

fetched live from OpenAlex

Abstract The Moravian Geographical Reports does not often publish Book Reviews (let alone essays), but this new book on “Renewable Energies and European Landscapes” 1 is a well-deserved exception to the rule! It is an edited collection of essays gathered together by Frolova (University of Granada, Spain), Prados (University of Sevilla, Spain) and Nadaï (Centre International de Recherche sur l’Environnement et le Développement: CIRED -CNRS, France), based on a series of Workshops organised under the auspices of several agencies (from both Spain and France) in the period from 2007 to the present. In particular, the Spanish Network on Renewable Energies and Landscape (RESERP) began in 2010, with an emphasis on wind and solar power. Published by a well-respected agency, the question can be clearly stated at the outset: Do the editors fulfil their ambitious agenda of providing case studies of value for the emerging research on landscapes of renewable energies of Europe, writ large, i.e. beyond the ‘Southern European’ environment? Or: what is the ‘added value’ of the Southern European cases?

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)0.003
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.817
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0040.002
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.063
GPT teacher head0.388
Teacher spread0.324 · 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.

Study designNot applicable
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

Citations2
Published2017
Admission routes1
Has abstractyes

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