Modernising Optimisation in Decision Making
Bibliographic record
Abstract
Optimisation in decision making has broad applicability to the whole spectrum of nuclear and radiation-related policy, regulation and practice. The way it is applied has changed in recent years as society has evolved to promote an inclusive and holistic decision-making process. In the radiological protection area, the increasing challenge is to apply it in the broader context of overall risk management, broaden the stakeholder participation process, and deepen the thinking on reasonableness in optimisation. Experience in various circumstances has shown that there is a need to develop a framework in which very different aspects can be balanced to support risk-based decision-making and determine the level of tolerance of risk and uncertainty. Currently, optimisation, as one of the three protection principles of the international radiological protection system, is well defined in theory, complex in practice in a large number of situations, and increasingly based on the combination of four pillars: holistic, or integrated, protection where optimisation of radiological protection is applied as part of an overall optimisation of protection from all relevant hazards (sometimes referred to as an ‘all hazards approach’); maximising net benefit and the ‘common good’ - taking all relevant socio-economic and environmental risks and impacts, and benefits, into account so that radiological protection facilitates and enhances well-being and does not result in unintended consequences; involving stakeholders – with stakeholder engagement being integral to the decision-making process and key for defining acceptance or tolerance; the need for proportionality – by implementing a graded approach. As radiological protection moves towards a more holistic approach, recognising its multi-dimensional nature, there should be an even greater focus on understanding and meeting the expectations of those affected by the application of protection. Development is therefore needed along three main lines: (i) how policy and practice are developed in consultation with, and understood by a wider cross section of society; (ii) how policy and practice could be simplified; (iii) how to integrate consideration of other policies and practices so that radiological protection is properly integrated into the wider decision-making process and not seen as a separate "add-on" process. The forthcoming work of the Committee on Radiological Protection and Public Health on modernising the way optimisation is implemented should help inform the revision of the ICRP system. The ultimate goal is to facilitate sustainable and transparent decision-making, beyond the optimisation of radiological protection, in the broader perspective of individual and social well-being. Text on behalf the NEA CRPPH's bureau.
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 | 0.026 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".