A Framework of Adaptive Risk Governance for Urban Planning
Bibliographic record
Abstract
The notion “risk governance” refers to an integrated concept on how to deal with public risks in general, and so-called complex, ambiguous and uncertain risks in particular. These ideas have been informed by interdisciplinary research drawing from sociological and psychological research on risk, Science and Technology Studies (STS) and research by policy scientists and legal scholars. The notion of risk governance pertains to the many ways in which many actors, individuals and institutions, public and private, deal with risks. It includes formal institutions and regimes and informal arrangements. The paper will first develop an adaptive and integrative framework of risk governance and applies this model to the risks of urban planning. After a short summary of the roots of risk governance, key concepts, such as simple, uncertain, complex and ambiguous risks, will be discussed. The main emphasis will be on each of the five phases of risk governance: pre-assessment, interdisciplinary assessment, risk evaluation, risk management and risk communication. The paper will explain how these phases of risk governance can be applied to the area of urban planning and improve the dynamic sustainability of cities.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".