Understanding change, complexities, and governability challenges in small-scale fisheries: a case study of Limbe, Cameroon, Central Africa
Bibliographic record
Abstract
Climate change, globalization, and increasing industrial and urban activities threaten the sustainability and viability of small-scale fisheries. How those affected can collectively mobilize their actions, share knowledge, and build their local adaptive capacity will shape how best they respond to these changes. This paper examines the changes experienced by small-scale fishing actors, social and governance complexities, and the sustainability challenges within the fisheries system in Limbe, Cameroon. Drawing on the fish-as-food framework, we discuss how ineffective fishery management in light of a confluence of global threats has resulted in changes to fish harvesters’ activities, causing shortages in fish supply and disruptions in the fish value chain. The paper uses focus group discussions with fish harvesters and fishmongers to present three key findings. First, we show that changes in the fisheries from increased fishing activities and ineffective fishery management have disrupted fish harvesting and supply, impacting the social and economic well-being of small-scale fishing actors and their communities. Second, there are complexities in the fisheries value chain due to shortages in fish supply, creating conflicts between fisheries actors whose activities are not regulated by any specific set of rules or policies. Third, despite the importance of small-scale fisheries in Limbe, management has been abandoned by fishing actors who are not well-equipped with the appropriate capacity to design and enforce effective fishery management procedures and protections against illegal fishing activities. Empirical findings from this understudied fishery make scholarly contributions to the literature on the fish-as-food framework and demonstrate the need to support small-scale actors’ fishing activities and the sustainability of the fisheries system in Limbe.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".