Quantitative stakeholder-driven assessment of radiation protection issues via a PIANOFORTE online survey
Bibliographic record
Abstract
To enhance stakeholder engagement and foster the inclusion of interests of citizens in radiation protection research, a comprehensive online survey was developed within the framework of the European Partnership PIANOFORTE. This survey was performed in 2022 and presented an opportunity for a wide range of stakeholders to voice their opinions on research priorities in radiation protection for the foreseeable future. Simultaneously, it delved into pertinent issues surrounding general radiation protection. The PIANOFORTE e-survey was conducted in the English language, accommodating a diverse range of participants. Overall, 440 respondents provided their insights and feedback, representing a broad geographical reach encompassing 29 European countries, as well as Canada, China, Colombia, India, and the United States. To assess the outcomes, the Positive Matrix Factorization numerical model was applied, in addition to qualitative and quantitative assessment of individual responses, enabling the discernment of four distinct stakeholder groups with varying attitudes. While the questionnaire may not fully represent all stakeholders due to the limited respondent pool, it is noteworthy that approximately 70% of the participants were newcomers to comparable surveys, demonstrating a proactive attitude, a strong willingness to collaborate and the necessity to continuously engage with stakeholder groups. Among the individual respondents, distinct opinions emerged particularly regarding health effects of radiation exposure, medical use of radiation, radiation protection of workers and the public, as well as emergency and recovery preparedness and response. In cluster analysis, none of the identified groups had clear preferences concerning the prioritization of future radiation protection research topics.
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 | 0.019 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".