How to start a network? Experience in networking and achievements of the IRPA Young Generation Network
Bibliographic record
Abstract
Since its establishment in 2018, the IRPA Young Generation Network (YGN) has created several dynamics under the framework of its Strategic Agenda to promote the representation of the young generation, professional development, experience transfer, relationship and communication of students, young professionals and scientists in radiation protection and its allied fields. This article first reports on the activities performed from 2018 until today, with highlights on some important events, collaborations and publications. The IRPA YGN have made these achievements with the essential support of its Leadership Committee, the various national Young Generation Networks, and the IRPA organization and its Associate Societies. Then, the insights and experiences obtained from these activities are discussed and used to inform how the IRPA YGN aims to achieve its on-going activities and continue to follow the ways paved in the Strategic Agenda 2022–2024. It is expected that the identification of the backbone elements supporting a young generation network and also the very specific challenges can be useful for the future management of the IRPA YGN and existing national YGN and inspire the creation of other young generation networks.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".