Building Local Resilience To Climate Change Vulnerability In Small-Scale Fishery Communities Of Lake Volta, Ghana.
Bibliographic record
Abstract
Climate change affects fishing globally, and the world’s 100 million small-scale fisheries (SSF) are no exception. Despite the high number of SSFs worldwide and their significant economic contributions, SSFs often go understudied. The SSF sector continues to suffer from rapid depletion of fish resources due to climate change, affecting livelihoods and increasing their vulnerability worldwide. This study aims to understand the climate vulnerabilities experienced by SSFs communities, the challenges to building resilience, and the nature of governance responses needed to address impacts in Lake Volta, Ghana. A qualitative research method was used in gathering information for this research. Secondary data collection methods were used instead of primary data collection due to the Covid-19 pandemic. A systematic literature review was used with the help of a reference management tool known as the Zotero to collect, analyze, and organize existing literature using search engines. Also, the pragmatic worldview and I-ADApT framework are adopted to understand the various variations of adaptations, governing systems, vulnerabilities and resilience strategies in SSFs communities affected by climate change. The study found that communities adapt to climate change impacts by diversifying their livelihoods and migrating to cities for alternative employment. However, their strategies are not integrated into policymaking and adaptation policies. The results of this study support designing policies fostering community adaptation and resilience and exceptional attention to local knowledge and participation in decision-making. The study recommends that policymakers embrace the essence of enhancing alternative livelihood strategies for the fishing communities in Lake Volta to help combat the effects of climate change. To this end, this study identifies the following as strategies to enhance adaptation and resilience through policy-making: facilitating the transition to alternative livelihoods, strengthening and building local capacities to reduce risk and vulnerability, responding to income uncertainty and fish stocks variations by countering climate change effects.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".