Operational safety economics: Foundations, current approaches and paths for future research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Due to the COVID-19 pandemic in 2020, the trade-off between economics and epidemic prevention (safety) has become painfully clear worldwide. This situation thus highlights the significance of balancing the economy with safety and health. Safety economics, considering the interdependencies between safety and micro-economics, is ideal for supporting this kind of decision-making. Although economic approaches such as cost-benefit analysis and cost-effectiveness analysis have been used in safety management, little attention has been paid to the fundamental issues and the primary methodologies in safety economics. Therefore, this paper presents a systematic study on safety economics to analyze the foundational issues and explore the possible approaches. Firstly, safety economics is defined as a transdisciplinary and interdisciplinary field of academic research focusing on the interdependencies and coevolution of micro-economies and safety. Then we explore the role of safety economics in safety management and production investment. Furthermore, to make decisions more profitable, economic approaches are summarized and analyzed for decision-making about prevention investments and/or safety strategies. Finally, we discuss some open issues in safety economics and possible pathways to improve this research field, such as security economics, risk perception, and multi-criteria analysis.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it