Marine social-ecological responses to environmental change and the impacts of globalization
Bibliographic record
Abstract
Marine social–ecological systems consist of interactive ecological and human social elements so that changes in ecological systems affect fishing-dependent societies and vice versa. This study compares the responses of marine ecological and fishing-dependent systems to environmental change and the impacts of globalization, using four case-studies: NE Atlantic (Barents Sea), NW Atlantic (Newfoundland), SE Atlantic (Namibia) and the equatorial Atlantic (Ghana). Marine ecological systems cope with short-time changes by altering migration and distribution patterns, changing species composition, and changing diets and growth rates; over the longer term, adaptive changes lead to increased turn-over rates and changes in the structure and function of the system. Fishing communities cope with short-term change through intensification and diversification of fishing, migration and ‘riding out the storm’. Over the longer term, adaptive changes in policy and fisheries governance can interact with social–ecological change to focus on new fisheries, economic diversification, re-training, out-migration and community closures. Marine social–ecological systems can ultimately possess rapid adaptive capacity in their ecological components, but reduced adaptive capacity in society. Maintaining the diversity of response capabilities on short and longer time scales, among both ecological and human fishing systems, should be a key policy objective. The challenge is to develop robust governance approaches for coupled marine social–ecological systems that can respond to short- and long-term consequences of global change.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".