Toward a Commonly Shared Public Policy Perspective for Analyzing Risk Coping Strategies
Bibliographic record
Abstract
The concept of risk has received scholarly attention from a variety of angles in the social, technical, and natural sciences. However, public policy scholars have not yet generated a comprehensive overview, shared understanding and conceptual framework of the main problem-solving approaches applied by governments in coping with risks. In this regard, our main aim is to examine existing perspectives on prevailing risk coping strategies, find a common denominator among them and contribute to current policy and risk science literature through providing a conceptual framework that systematically spans the spectrum of risk coping strategies and incorporates the essence of the most relevant insights. To this end, we first examine the concept of risk in-depth by exploring various definitions and types of risk. We then review different approaches proposed by different strands of research for addressing risk. Finally, we assess current knowledge and develop an amalgamated perspective for examining how risks can be addressed by classifying them into six general types of response (no response; prevention; control; precaution; toleration; and adaptation) as well as indicators to identify these responses. We argue that these strategies can function as a heuristic tool for decisionmakers in designing appropriate policies to cope with risks in decision-making processes.
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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 | 0.043 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.018 | 0.009 |
| Science and technology studies | 0.006 | 0.037 |
| Scholarly communication | 0.025 | 0.025 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".