15 Years of the European Nuclear Education Network (ENEN Association)
Bibliographic record
Abstract
The European Nuclear Education Network (ENEN) was established in 2003 through an EU Fifth Framework Programme (FP) project, as a legal nonprofit-making body. Its main objective is the preservation and further development of expertise in the nuclear fields by higher education and training. This objective is realized through the cooperation between EU universities involved in education and research in nuclear disciplines, nuclear research centers and the nuclear industry. As of March 2018, ENEN has 66 members in 18 EU countries and has concluded Memoranda of Understanding (MoU) with partners beyond Europe for further cooperation, including organizations in, Russian Federation, Ukraine, Canada and Japan. ENEN also has good collaboration with national networks and international organizations such as the Belgian Nuclear Education Network (BNEN) and the International Atomic Energy Agency (IAEA). The main activities developed, and results achieved, within the first 15 years of the ENEN Association are presented and discussed. These include, for example, the launch of the European Master of Science in Nuclear Engineering (EMSNE), the annual ENEN Ph.D. competition and the portfolio of more than 10 EURATOM projects dealing with nuclear education, training and knowledge management through development of teaching methods and materials, courses, and exchange of students and teachers within EU and beyond. Those projects were all supported by the European Commission with the ENEN Association acting as the coordinator or as a partner.
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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 | 0.011 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.015 |
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".