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.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".