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Record W4387038408 · doi:10.1049/icp.2023.0350

Gender equality in the distribution sector

2023· article· en· W4387038408 on OpenAlexaff
S. Ouziaux

Bibliographic record

VenueIET conference proceedings. · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPublic Procurement and Policy
Canadian institutionsImpact
Fundersnot available
KeywordsDistribution (mathematics)Computer scienceBusinessMathematics

Abstract

fetched live from OpenAlex

Gender Equality is a core value of the European Union, a fundamental right and a key driver of economic growth and social well-being. However, the European Union's energy sector remains unequal. According to the collected information in a European Study, the energy sector remains dominated by male workers, who represented 80% of total workforce in 2019. The EU's New Gender Equality Strategy puts the onus on policy makers to understand and address women's needs and abilities to be active in the energy sector. The companies where leadership and governance are dominated by men often neglect the women's perspectives of the society. Consequently, the lack of proactive participation of women in the strategic decision may lead to negative impacts, such as, for electric utilities, inaccurate identification of consumers' electricity needs and inappropriate pricing. Moreover, whether in developed or developing economies, in a household, the electricity is not used for the same purposes for women and men. If the subject was not really a topic of design in electricity distribution infrastructure in Europe, there is now an opportunity in modernization or reconstruction projects to incorporate a gender perspective into each stage of a project cycle. In this paper, The ENGIE Impact experts introduce the concept of gender mainstreaming in the electricity distribution sector, analyse the current situation in the Energy sector based on a study carried out for the European commission and propose a four-step plan to include gender perspective within new distribution projects.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.671
Threshold uncertainty score0.818

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.116
GPT teacher head0.294
Teacher spread0.178 · 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 designTheoretical or conceptual
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

Citations0
Published2023
Admission routes1
Has abstractyes

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