Bibliographic record
Abstract
The last few decades have seen increasingly widespread use of risk assessment and management techniques as aids in making complex decisions. However, despite the progress that has been made in risk science, there still remain numerous examples of risk-based decisions and conclusions that have caused great controversy. In particular, there is a great deal of debate surrounding risk assessment: the role of values and ethics and other extra-scientific factors, the efficacy of quantitative versus qualitative analysis, and the role of uncertainty and incomplete information. Many of the epistemological and methodological issues confronting risk assessment have been explored in general systems theory, where techniques exist to manage such issues. However, the use of systems theory and systems analysis tools is still not widespread in risk management. This article builds on the Alachlor risk assessment case study of Brunk, Haworth, and Lee to present a systems-based view of the risk assessment process. The details of the case study are reviewed and the authors' original conclusions regarding the effects of extra-scientific factors on risk assessment are discussed. Concepts from systems theory are introduced to provide a mechanism with which to illustrate these extra-scientific effects The role of a systems study within a risk assessment is explained, resulting in an improved view of the problem formulation process The consequences regarding the definition of risk and its role in decision making are then explored.
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 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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".