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Record W4410206946 · doi:10.1139/facets-2024-0120

Risk equivalent safe operating space as an inclusive framework for living resource management in a multisectoral, multicultural world

2025· article· en· W4410206946 on OpenAlexaffvenue
Jake Rice, Daniel E. Duplisea, Karen L. Hunter, Marie‐Julie Roux

Bibliographic record

VenueFACETS · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsFisheries and Oceans Canada
Fundersnot available
KeywordsMulticulturalismSpace (punctuation)Resource (disambiguation)BusinessRisk analysis (engineering)Computer sciencePolitical science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.097
Threshold uncertainty score0.844

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.013
GPT teacher head0.336
Teacher spread0.322 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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