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Record W2540359365 · doi:10.1017/s1867299x00002907

Incorporating Social Sciences in Public Risk Assessment and Risk Management Organisations

2014· article· en· W2540359365 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEuropean Journal of Risk Regulation · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicRisk Perception and Management
Canadian institutionsnot available
Fundersnot available
KeywordsValue (mathematics)Social riskPublic relationsRisk managementSet (abstract data type)Political scienceSocial scienceSociologyBusinessActuarial scienceFinance

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.545
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.038
GPT teacher head0.319
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it