ENGIE - promoting gender balance in the area of earth science and engineering
Bibliographic record
Abstract
The Raw Materials Community of the European Institute of Innovation and Technology (EIT RM) is supporting the implementation of a project which aims to attract 13-18 year old girls to study geosciences and related engineering disciplines, with the objective of improving the gender balance in these fields at entry level to tertiary education and the workplace. The project ‘ENGIE – Encouraging Girls to Study Geosciences and Engineering’ will focus on informing and inspiring secondary school female students as career decisions are made generally in this period of their lives. It started in January 2020 and will last for three years. ENGIE will support awareness raising activities in more than 20 European countries to encourage 13-18 years old girls to study geosciences and geo-engineering. Public bodies, schools, research centres, universities, professional organisations and on gender equality will be brought together, and strategies will be formulated on the basis of European and international benchmarking. Best practices and success stories will be taken over from countries where STEM education and geo-sciences have already been successfully promoted among young women (Australia, Canada, US) and also from leading European countries in this area, such as Sweden or Finland. Experiences gained during the implementation of national actions will be used for the formulation of longer-term strategies so that the expected higher interest for these professions can be satisfied by proper education and career opportunities in Europe. The ENGIE project will focus on raising the girls’ interest in a well-defined area: geosciences and geo-engineering. This will help the project partners to formulate very clear messages. One of the challenges in supporting gender equality in research is the shortage of knowledge on how to effectively encourage and sustain ayoung woman’s interest in STEM. ENGIE will address this issue by conducting research and gathering comprehensive knowledge on what keeps women away from geosciences and engineering. In the frame of the project, an extensive communication strategy will be developed and progress will be monitored. Innovative approach of this project relies on the creation of a platform for the co-operation between competent international partners, who are strongly interested in tackling this shortage (future employers inclusive). ENGIE will be implemented by the cooperation of 26 institutions. The partnership involves 3 universities (University of Miskolc, Luleå University of Technology and University of Zagreb), 2 research centres (Italian National Research Council and La Palma Research Centre) and a European-level professional geoscience organisation (European Federation of Geologists). 20 national member associations of EFG will also take part in the project implementation as Linked Third Parties. By their contribution, the project activities will be extended to more than 20 European countries.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.010 |
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".