The ESA Education Programme and its ESA Academy
Bibliographic record
Abstract
The European Space Agency’s Education Programme, composed of the Primary and Secondary STEM Education Programme for younger students, and the ESA Academy Programmefor university students, is strongly committed not only to inspire,butalsoto actively engagestudents. The Primary and Secondary STEM Education Programme’s aim is to use space as a teaching context to enhance youngsters’ literacy, skills and competences as well as to develop the pupils’ core values and attitudes in STEMdisciplines, and to inspire and to motivate them to pursue studies and careers in the STEM sector. The ESA Academy, the overarching education programme for university students, uses space asthe subject, and is designed to equip the next generation of professionals working inthe space sectorwith 21st century skills and competences,with the objective of enhancingtheir employability,and stimulatingtheir creativity, innovativenessand entrepreneurship.The ESA Academy encompassesa portfolio of hands-on ‘Space’ projects ranging from scientific and technology-demonstration experiments to be run on a number of different professional platforms, to small satellite missions such as CubeSats; complimented by a varied portfolio of training sessions given by space professionals coming from all fields of ESA’s expertise, as well as from space industry and academia.Every year hundreds of students participate in ESA Academy’sactivities, with students participating in launch and experiment campaigns conducted at state of the art facilities located at several centres of excellence around Europe, and amassing an impressive portfolio of space-relatedand research experience. In order to be eligible to participate in the ESA Academy programmes, students must be nationals of one of the 22 ESA Member States, or Canada or Slovenia. Operating with students coming from across 24 different statesand at different levels of their university studies, comes with a unique set of challenges, including, but not limited to, interacting with different national academic approaches, different academic schedules, student engagement levels, gender and inclusiveness, and team funding. The Education Office has risen to these challenges and has developed a comprehensive and inclusive programme framework, which continues to develop as new challengesand new opportunitiesare identified. The ESA Academy is moving forward with the confidence that the future generations of space professionals in the ESA Member States may benefit from getting the best training and hands-on experience to supportthe future of the European space sector. The ESA Academy aims to reinforce, and even to further develop, its offering of programmes and training sessions over the coming years.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.119 | 0.062 |
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".