Incorporating Social Sciences in Public Risk Assessment and Risk Management Organisations
Bibliographic record
Abstract
The objective of the article is to analyse the use of Humanities and Social Sciences (HSS) in public risk assessment and risk management organisations in France, Germany, the UK, the Netherlands, Canada and the United States based on more than a hundred interviews conducted with social sciences experts employed by or working for these organisations. If the added value brought by the integration of social scientists is recognised, the use of social sciences differs from one organisation to another. The article compares the different positions given to social scientists inside and outside the organisation, the various methods used and the different contents produced. The survey highlights a set of initiatives that are scattered, differentiated and ultimately have little in common – except that they often play a marginal role in the main activities of the agencies concerned.
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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 | 0.025 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".