Using Structured Decision Making to Help Implement a Precautionary Approach to Endangered Species Management
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
Endangered species protection is a significant risk management concern throughout North America. An extensive conceptual literature emphasizes the role to be played by precautionary approaches. Risk managers, typically working in concert with concerned stakeholders, frequently cite the concept as key to their efforts to prevent extinctions. Little has been done, however, to evaluate the multidimensional impacts of precautionary frameworks or to assist in the examination of competing precautionary risk management options as part of an applied risk management decision framework. In this article we describe how decision-aiding techniques can assist in the creation and analysis of alternative precautionary strategies, using the example of a multistakeholder committee charged with protection of endangered Cultus Lake salmon on the Canadian west coast. Although managers were required to adopt a precautionary approach, little attention had been given to how quantitative analyses could be used to help define the concept or to how a precautionary approach might be implemented in the face of difficult economic, social, and biological tradeoffs. We briefly review key steps in a structured decision-making (SDM) process and discuss how this approach was implemented to help bound the management problem, define objectives and performance measures, develop management alternatives, and evaluate their consequences. We highlight the role of strategy tables, employed to help participants identify, alternative management options. We close by noting areas of agreement and disagreement among participants and discuss the implications of decision-focused processes for other precautionary resource management efforts.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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