Sharpening resilience concepts to catalyze advances in marine social-ecological systems research
Bibliographic record
Abstract
Abstract Marine social-ecological systems (SES) are increasingly affected by anthropogenic stressors such as climate change, fisheries, pollution, and habitat degradation. The responses of these complex adaptive systems, and the interactions between their ecological and social components, are still not fully understood. Resilience, vulnerability, adaptive capacity, and tipping points capture essential aspects of SES dynamics, but their heterogeneous use within the marine research community hampers progress toward integrative understanding and effective sustainable governance. Drawing from a session at MSEAS 2024, subsequent participatory activities, and a focused literature review, we examine how resilience-related concepts in marine SES are defined and assessed. We propose recommendations to guide resilience-related studies in marine SES: (1) begin with clear definitions of resilience-related concepts and underlying theory; (2) define the system, its components and boundaries, as well as the temporal and spatial scales of analysis; (3) contextualize the used methods or indicators within the wider SES research landscape; and (4) adopt a more holistic SES view by accounting for effects on system components beyond the primary focus of the study. The use of a shared set of guiding principles in marine SES research would strengthen conceptual coherence, facilitate cross-system comparisons, and support interdisciplinary integration in marine science.
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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 | 0.037 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.003 | 0.031 |
| Scholarly communication | 0.009 | 0.020 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".