Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".