Untangling social–ecological interactions: A methods portfolio approach to tackling contemporary sustainability challenges in fisheries
Bibliographic record
Abstract
Meeting the objectives of sustainable fisheries management requires attention to the complex interactions between humans, institutions and ecosystems that give rise to fishery outcomes. Traditional approaches to studying fisheries often do not fully capture, nor focus on these complex interactions between people and ecosystems. Despite advances in the scope and scale of interactions encompassed by more holistic methods, for example ecosystem-based fisheries management approaches, no single method can adequately capture the complexity of human-nature interactions. Approaches that combine quantitative and qualitative analytical approaches are necessary to generate a deeper understanding of these interactions and illuminate pathways to address fisheries sustainability challenges. However, combining methods is inherently challenging and requires understanding multiple methods from different, often disciplinarily distinct origins, demanding reflexivity of the researchers involved. Social-ecological systems' research has a history of utilising combinations of methods across the social and ecological realms to account for spatial and temporal dynamics, uncertainty and feedbacks that are key components of fisheries. We describe several categories of analytical methods (statistical modelling, network analysis, dynamic modelling, qualitative analysis and controlled behavioural experiments) and highlight their applications in fisheries research, strengths and limitations, data needs and overall objectives. We then discuss important considerations of a methods portfolio development process, including reflexivity, epistemological and ontological concerns and illustrate these considerations via three case studies. We show that, by expanding their methods portfolios, researchers will be better equipped to study the complex interactions shaping fisheries and contribute to solutions for sustainable fisheries management.
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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 | 0.197 | 0.155 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.020 | 0.019 |
| Open science | 0.007 | 0.018 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".