Bibliographic record
Abstract
Differences in the degree of social or cultural acceptance of events or states of the world, political sensitivities, economic and distributive consequences have important impacts on the way evaluations of risk are carried out and on risks management. Whilst there is a recognition that enhancing co-ordination on the scientific dimension of risk assessments should not necessarily lead to a common political response on how to manage risks, there is also a strong concern that divergences on risk assessment methodologies and in the terminology used to express assessments of risk and uncertainty are hindering sound risk governance. To address some of these issues, individuals within the European Commission and the governments of the United States and Canada initiated a Transatlantic Risk Dialogue early 2008, and later broadened it to create a Global Risk Assessment Dialogue. This case study illustrates how collaboration has been developing through dialogue and collaborative work between members of the scientific community within government agencies and in research institutions.
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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".