Risk equivalent safe operating space as an inclusive framework for living resource management in a multisectoral, multicultural world
Bibliographic record
Abstract
Governance processes for management of living resources are increasingly inclusive and participatory, with more use of integrated risk-based approaches. Progress has been challenged by diverse participants holding different values, evidence rooted in different knowledge systems, and participants placed in adversarial roles. Drawing together developments in risk equivalence, the concept of safe operating space, and viability theory, Risk Equivalent Safe Operating Spaces address these challenges. Within the framework diverse perspectives can express their desired ecological, economic, and social outcomes using their own values and indicators. The aggregate suite of all indicators delineates a multidimensional space within which each perspective can describe their relative risk tolerances along each axis, using evidence from all relevant knowledge systems. The “present state” of the socio-ecological system is identified within this space, along with zones of equivalent risk for each perspective, and (if it exists) a zone of Safe Operating Space (SOS) within some acceptable risk tolerance for all perspectives. Pathways can be developed that first seek equivalent risk for all perspectives, then lead towards the center of the common, shared SOS. Where certain perspectives or dimensions of the multidimensional space have explicit priority, the pathways can prioritize minimizing these risks.
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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 | 0.015 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.007 | 0.036 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.004 | 0.007 |
| 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".