Responding to civil war: fisheries as a safety net and lootable resource on Lake Tanganyika, the Democratic Republic of Congo
Bibliographic record
Abstract
Research on conflict and fisheries has largely focused on conflict between resource users, rather than on how fisheries are affected by external conflict, including civil war. Knowledge that does exist does not fully engage with the specific characteristics of conflicts, how those characteristics affect fisheries, and how fishers respond. This article identifies how the characteristics of conflict in the Democratic Republic of the Congo (DRC) affect the fisheries of the transboundary Lake Tanganyika and how those dependent on small-scale fisheries have responded to those characteristics. Data was collected at three fish landing sites through remote interviews in 2017 and 2018. The results show that the primary characteristic of the DRC conflict is the sporadic and unpredictable nature of the violence generating insecurity, loss of equipment and increase in fishing pressure. Increasing fishing pressure is associated with newcomers, who turn to fishing as a safety net, yet do not abide by local norms and beliefs. A reported increase in illegal fishing and corruption further present challenges to the weakly managed fisheries. The research concludes that the experience of civil war brings multiple and contrasting sources and experiences of vulnerability for fishers. The significant influence that conflict has on fisherfolk and fisheries supports calls for greater recognition of how the wider political and economic environment of natural resources affects how they are used and governed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".