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Record W2965109336 · doi:10.1111/risa.13383

The Coming of Age of Risk Governance

2019· article· en· W2965109336 on OpenAlexaff
Andreas Klinke, Ortwin Renn

Bibliographic record

VenueRisk Analysis · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicRisk Perception and Management
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsRisk governanceRisk managementDeliberationCorporate governanceConceptualizationRationalityIT risk managementRisk assessmentRisk analysis (engineering)IT riskEnterprise risk managementPolitical riskManagement scienceSociologyPolitical scienceBusinessPoliticsEconomicsComputer scienceLawManagement

Abstract

fetched live from OpenAlex

Proposed as an advanced conceptualization of how to handle risk, risk governance begins with the critique and expansion of the traditional idea and standard practices of risk analysis. In developments over the last two decades, proponents of a more integrative approach on governing risks have moved further away from distinct conceptions of risk assessment, risk management, and risk communication and toward the processes and institutions that guide, restrain, and integrate collective activities of handling risk. In early formulations of what risk governance entails, the superiority of the interplay between risk evaluation and risk management over linear and simple deductions from risk assessment to risk management was established precisely by developing a distinctive rationality of how to proceed. Later, the International Risk Governance Council recaptured this distinctive rationality that institutionalized processes should embody the interplay of the assessment of risks and related concerns, their sociopolitical appraisal, and the logical inference for risk management. Recently, this approach has been refined and augmented toward an integrative and adaptive concept of risk governance and toward a postnormal conception of risk governance. Main characteristics are a new concept of differentiated responsibility and deliberation in which expertise, experience, and tacit knowledge are integrated, forming the core of legitimate political risk decision making.

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)0.020
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0040.047
Scholarly communication0.0140.027
Open science0.0020.008
Research integrity0.0070.014
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.008
GPT teacher head0.283
Teacher spread0.274 · 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 designTheoretical or conceptual
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

Citations104
Published2019
Admission routes1
Has abstractyes

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