Rivers as resources, rivers as borders: community and transboundary management of fisheries in the Upper Zambezi River floodplains
Bibliographic record
Abstract
This article examines the recent convergence of community‐based and transboundary natural resource management in Africa. We suggest that both approaches have potential application to common‐pool resources such as floodplain fisheries. However, a merging of transboundary and community‐based management may reinforce oversimplifications about heterogeneity in resources, users, and institutions. A scalar mismatch between the ecosystem of concern in transboundary management and local resources of concern in community‐based management, as well as different colonial and post‐colonial histories contribute to this heterogeneity. We describe a fishery shared by Namibia and Zambia in terms of hybrid fisheries management. We examine settlement patterns, fishermen characteristics, sources of conflict, and perceptions regarding present and potential forms of fisheries management in the area. We also consider the implications that initiatives to manage resources on the local and ecosystem scale have for these fishing livelihoods. Our findings indicate that important social factors, such as the unequal distribution of population and fishing effort, as well as mixed opinions regarding present and future responsibility for fisheries management will complicate attempts to implement a hybrid community‐transboundary management initiative .
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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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| 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".