Adaptive capacity of small‐scale coastal fishers to climate and non‐climate stressors in the Western region of Ghana
Bibliographic record
Abstract
Small‐scale coastal fisheries (SSCF) in the Western region of Ghana are affected by a combination of climate and non‐climate stressors. Coastal communities are particularly vulnerable to these stressors because of their proximity to the sea and high dependence on small‐scale fisheries for their livelihoods. A better understanding of how fishing communities, particularly SSCF, respond to climate and non‐climate stressors is paramount to improve planning and implementation of effective adaptation action. Drawing on the capitals framework, this study examines the adaptive capacity of SSCF to the combined effects of climate‐related (increasing coastal erosion, and wave and storm frequency) and non‐climate‐related stressors (declining catches; scarcity and prohibitive cost of fuel; inconsiderate implementation of fisheries laws and policies; competition from the oil and gas industry; sand mining; and algal blooms). The findings show how fishers mobilise and use adaptive capacity through exploitation of various forms of capital, including cultural capital (e.g., local innovation); political capital (e.g., lobbying government and local authorities); social capital (e.g., collective action); human capital (e.g., local leadership); and natural capital (e.g., utilising beach sand) to respond to multiple stressors. Nevertheless, in many cases, fishers’ responses were reactive and led to negative (maladaptive) outcomes. Furthermore, this study underscores the importance of critically considering the interactive nature of capitals and how they collectively influence adaptive capacity in the planning and implementation of adaptation research, policy and practice.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".