Risk analysis versus risk governance: the case study of the Ebola Virus Disease
Bibliographic record
Abstract
Risk notions mostly espoused by the world risk society and securitization theories have influenced the two major risk handling methods: risk analysis and risk governance. Engaging the risk notions, some scholars and policy makers have identified risk governance as superior to risk analysis. Risk analysis, considered the classical method, has technical parameters, leaving out important societal considerations. Risk governance, an emerging method, reaches beyond technical into societal parameters, so it is more holistic. This risk analysis-governance distinction prompts the question on what exactly risk governance adds to risk analysis. To answer the question, the article uses methodology and concepts in policy studies: qualitative methods, mainly a policy analysis of the 2013/2014 Ebola Virus Disease (EVD) outbreak as a case study and synthesis of relevant bodies of literature, backed by secondary data from institutional and country sources; and the adaptive and integrative risk governance model of Klinke and Renn (2012 Klinke, A., and O. Renn. 2012. “Adaptive and Integrative Governance on Risk and Uncertainty.” Journal of Risk Research 15 (3): 273–292. doi:10.1080/13669877.2011.636838.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) as a conceptual framework to guide the policy analysis. The claim is that, depending on the model, risk governance mainly adds components that incorporate multilevel and multistakeholder participation to enhance risk handling. The overall finding in support of this claim is that risk governance, as more entrenched in international risk handling, considerably allows both multistakeholder and multilevel participation under its components, while risk analysis, generally dominating national risk handling, does not allow substantial multistakeholder participation under its components, although it appears that it could considerably allow multilevel participation as well. Despite the additions of risk governance to risk analysis, as practiced, both methods fail to be as inclusive as possible, suggesting there is room for improvement to risk handling.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".