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Record W4391352078 · doi:10.2903/sp.efsa.2024.en-8588

ENhanced COMmunication in Risk ANalysis (ENCOMRAN): Final report

2024· article· en· W4391352078 on OpenAlexfundno aff
Mats Gunnar Andersson, Josefine Elving, Erik Nordkvist, Anneluise Mader, Axel Menning, J. Kowalczyk, Ann‐Kathrin Lindemann, Milena Zupaniec, Till Bueser, Leonie Dendler‐Rafael, Pirkko Tuominen, Suvi Joutsen, Johanna Suomi, Kirsi‐Maarit Siekkinen, H.J. van der Fels‐Klerx, João Augusto Rossi Borges, Denise Koeppe

Bibliographic record

VenueEFSA Supporting Publications · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicRisk Perception and Management
Canadian institutionsnot available
FundersÉcole Polytechnique Fédérale de LausanneHealth CanadaEuropean CommissionEuropean Food Safety AuthorityWorld Health Organization
KeywordsRisk communicationComputer scienceRisk analysis (engineering)Business

Abstract

fetched live from OpenAlex

Risk analysis is a structured approach comprising risk assessment, risk management and risk communication, where the latter includes communication between risk assessors and risk managers. In recent decades there has been a shift in paradigm from “strict separation of risk assessment and risk management” to a model where dialogue is an integral part of the process. However, as the REFIT evaluation concluded, there is still a need for closer cooperation and coordination. The goals of the ENCOMRAN project were (i) to capture and describe interactions within food and feed risk analysis at EU Member State level as perceived by risk managers and risk assessors and (ii) to summarise the lessons learned in a document that could support communication and pinpoint crucial steps in communication and procedures to overcome identified obstacles. An internet-based survey was performed with risk assessors (n = 55), risk managers (n = 31) and those doing both (n = 9) in European countries. The results supported recent calls for more open, frequent, informal, feedback-rich and trust-building communication between risk assessors and risk managers. It is important to consider barriers to interaction, in particular how the prescribed independence of the scientific process is interpreted. Time management is also a key factor, especially in longer assessments. The results did not identify any one organisational model which was better than others. Rather, they indicated that it is important to provide comprehensive guidance on the appropriate level of communication at different stages of risk analysis and, importantly, the steps needed to guarantee independence and transparency of risk assessment. The results, together with interviews in the previous COMRISK project and literature data, were used as the basis for a guidance document. Overall, we recommend closer communication in framing assessment questions and full documentation of meetings to avoid misunderstandings regarding risk managers’ needs and possible deliverables.

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 imitation

Not 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.

metaresearch head score (Codex)0.031
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.031
Threshold uncertainty score0.165

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.027
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0050.004
Open science0.0030.007
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0250.008

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 head0.043
GPT teacher head0.407
Teacher spread0.364 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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