Trading Fast and Slow: Fish Marketing Networks Provide Flexible Livelihood Opportunities on an East African Floodplain
Bibliographic record
Abstract
Domestic marketing networks in inland small-scale fisheries (SSF) provide food and income to millions of the rural poor globally. Yet these contributions remain undervalued, as most trade is informal and unmonitored, and inland fisheries overlooked in research and policy. Taking a commodity chain approach, we provide a case study of access arrangements governing how people come to enter and benefit from the freshwater fish trade on Tanzania's Rufiji River floodplain. We conducted a repeat market survey, interviews, and participant observation with actors at all levels of the district trade over 15 months. Gender, age, and social capital structured participation patterns, with younger men dominating the more lucrative but riskier fresh trade, older men prioritizing steady income from smoked fish, and women culturally constrained to selling a “cooked” product (i.e., fried fish). Nearly all participants were local, with traders drawing on a complex web of relationships to secure supplies. The majority of market vendors cited the trade as their household's most important income source, with women's earnings and consumption of unsold fish likely to have substantial benefits for children's well-being. Our findings reveal a resilient and pro-poor trade system where, starting with small initial investments, people overcame considerable environmental, financial, regulatory, and infrastructural challenges to reliably deliver fish to rural and urban consumers. Preserving the ecological integrity of Rufiji wetlands in the face of hydro-power development and climate change should be a priority to safeguard the livelihoods and well-being of local inhabitants.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".