Development of a Methodology for Pooling Resources and Optimising Investments in the Field of CBRN Training and Capacity Building
Bibliographic record
Abstract
Deterrence, preparedness, and response to evolving chemical, biological, radiological, and nuclear CBRN threats are being strengthened by international communities and states.These threats require closer top-down and bottom-up cooperation at all levels in order to enable collaborative shared efforts, foster an environment for learning from one another, pool resources and expertise, and take advantage of synergies with an ultimate objective of improving institutional and collective safety and security.Improving preparedness and response necessitates closer and stronger interactions among various stakeholders, including security practitioners, researchers, policy makers, innovation providers, small and medium-sized enterprises (SMEs), and industry.In this context, forging a dynamic multidisciplinary CBRN network could be a more effective approach to promote synergies and addressing the need for stronger cooperation.A resource pooling strategy helps build a highly cooperative CBRN stakeholder community and ensures the network's long-term viability and sustainability.This study examines practical methods to ensure sustainability in pooling and sharing resources, knowledge, and best practices in the field of CBRN training and capacity building.It details how a solution-oriented strategy was created, including a generalised method for combining resources and maximising institutional and government investments.This study was conducted by combining the results of a literature review on best practices in resource pooling and sharing, determining its applicability to CBRN defence, and examining the Horizon 2020 project, European Network of CBRN Training Centres (eNOTICE), as a case study to draw real-time input from the project activities.This work proposes a novel and strategic contribution to the field of CBRN defence in the form of a practice-oriented concept and methodology, for establishing and maintaining CBRN networks at local, regional, and global levels.This top-down and bottom-up transversal strategy offers a way forward that can help CBRNrelated dynamic interdisciplinary networks to establish, maintain, and develop in a sustainable way.
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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 | 0.045 | 0.066 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".