Modelling future safe and just operating spaces in regional social-ecological systems
Bibliographic record
Abstract
Shaping social-ecological systems towards sustainable, desirable and equitable futures is often hampered by complex human-natural feedbacks, emergence and nonlinearities. Consequently, the future of systems vulnerable to collapse is uncertain under plausible trajectories of environmental change, socioeconomic development and decision-making. We develop a modelling approach that incorporates driver interactions and feedbacks to operationalise future "safe and just operating spaces" for sustainable development. Monte Carlo simulations of fish catch from India's Chilika lagoon are compared to conditions that are ecologically and socioeconomically desirable as per today's norms. Akin to a satellite-navigation system, the model identifies multidimensional pathways giving at least a 75% chance of achieving the desirable future, whilst simultaneously diverting the system away from undesirable pathways. Critically for regional governance, the driver limits and trade-offs associated with regulating the resource are realised. More widely, this approach represents an adaptable framework that explores the resilience of social-ecological interactions and feedbacks underpinning regional sustainable development.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".