Climate change adaptation and adaptive efficacy in the inland fisheries of the Lake Victoria basin
Bibliographic record
Abstract
Abstract Inland fisheries support the livelihoods of millions of people in riparian communities worldwide but are influenced by increasing climate variability and change. Freshwater fishing societies are among the most vulnerable to climate change given their dependence on highly threatened aquatic resources. As climate change intensifies, building adaptive capacity within communities and understanding the efficacy of adaptive strategies for maintaining household stability is essential for coping with ongoing social and environmental change. In this study, we examined household perceptions of climate change, livelihood impacts and responses to socio‐ecological changes in fishing‐dependent households in the Lake Victoria basin in Uganda, East Africa. Through a series of household surveys and focus group discussions in five fish landing sites, we assessed social adaptive capacity (SAC) based on 207 households and identified adaptive strategies that are effective for coping with climatic change. We found that people in fishing households are aware of environmental change but that most households do not have adaptive strategies that are efficacious for securing long‐term income and food security. We also investigated household demographics that contribute to SAC, examined links between SAC and adaptive efficacy and established potential routes towards developing effective adaptive approaches in small‐scale fisheries. This work contributes to a growing foundation of documented community‐based knowledge for building adaptive capacity in inland fisheries and the communities around the world that depend on them. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".