How Solid Is the Dutch (and the British) National Risk Assessment? Overview and Decision‐Theoretic Evaluation
Bibliographic record
Abstract
Internationally, national risk assessment (NRA) is rapidly gaining government sympathy as a science-based approach toward prioritizing the management of national hazards and threats, with the Netherlands and the United Kingdom in leading positions since 2007. NRAs are proliferating in Europe; they are also conducted in Australia, Canada, New Zealand, and the United States, while regional RAs now exist for over 100 Dutch or British provinces or counties. Focused on the Dutch NRA (DNRA) and supported by specific examples, summaries and evaluations are given of its (1) scenario development, (2) impact assessment, (3) likelihood estimation, (4) risk diagram, and (5) capability analysis. Despite the DNRA's thorough elaboration, apparent weaknesses are lack of stakeholder involvement, possibility of false-positive risk scenarios, rigid multicriteria impact evaluation, hybrid methods for likelihood estimation, half-hearted use of a "probability × effect" definition of risk, forced comparison of divergent risk scenarios, and unclear decision rules for risk acceptance and safety enhancement. Such weaknesses are not unique for the DNRA. In line with a somewhat reserved encouragement by the OECD (Studies in Risk Management. Innovation in Country Risk Management. Paris: OECD, 2009), the scientific solidity of NRA results so far is questioned, and several improvements are suggested. One critical point is that expert-driven NRAs may preempt political judgments and decisions by national security authorities. External review and validation of major NRA components is recommended for strengthening overall results as a reliable basis for national and/or regional safety policies. Meanwhile, a broader, more transactional concept of risk may lead to better national and regional risk assessments.
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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 | 0.034 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".