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Record W625469181

Smart, Sustainable and Inclusive Growth : How Renewable Energy and Clean Tech can contribute to the Europe 2020 Strategy.

2011· article· en· W625469181 on OpenAlexaboutno aff
Ole Damsgaard, Aslı Tepecik Diş

Bibliographic record

VenueKTH Publication Database DiVA (KTH Royal Institute of Technology) · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicRegional Development and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsRenewable energyContext (archaeology)Sustainable developmentBusinessPolitical scienceEuropean unionEconomic growthPrivate sectorEngineeringEconomic policyEconomicsGeography
DOInot available

Abstract

fetched live from OpenAlex

Main messages from the Mid Sweden Conference 14 – 15 June 2011. The County Administrative Boards and County Councils of Jämtland and Västernorrland in Sweden organized the second Regional Development Conference aimed at addressing the objectives of the Europe 2020 strategy in the context of renewable energy and clean technologies. The conference was held in Östersund on the 14th and 15th of June, 2011 with participants from different levels of governmental units, public and private sector, academia and NGOs in Europe as well as Canada. Main messages from the conference emphasized synergies between the clean technologies and renewable energy sectors that will help regional development strategies promote the EU 2020 strategy 'Smart, sustainable and inclusive Europe'. To reach the ambitious objectives set in the EU 2020 strategy, it is essential to have a bottom up approach while the objectives, strategies and measures need to be deeply rooted in strong communication among the local, regional and national levels. The role of regional and local authorities in this regard, is imperative to make use of the regional and local conditions and to establish innovative methods for integrating and utilizing local strengths and possibilities.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.858
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.021
GPT teacher head0.259
Teacher spread0.238 · 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 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

Citations1
Published2011
Admission routes1
Has abstractyes

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