Fish Losses for Whom? A Gendered Assessment of Post-Harvest Losses in the Barotse Floodplain Fishery, Zambia
Bibliographic record
Abstract
Few studies examine post-harvest fish losses using a gender lens or collect sex-disaggregated data. This mixed-methods study assessed fish losses experienced by female and male value chain actors in a fishery in western Zambia to determine who experiences losses, why, and to what extent. Results indicate that participation in the fishery value chain is gendered and most losses occur during post-harvest activities. Discussions with fishers, processors, and traders suggest the value chain is more fluid than often depicted, with people making calculated decisions to sell fresh or dried fish depending on certain conditions, and mostly driven by the need to avoid losses and attain higher prices. The study shows that gender norms shape the rewards and risks offered by the value chain. This could be the reason why a greater proportion of women than men experienced physical losses in our study sample. Female processors lost three times the mass of their fish consignments compared to male processors. Technical constraints (lack of processing technologies) and social constraints (norms and beliefs) create gender gaps in post-harvest losses. Addressing unequal gender relations in value chains, whilst also promoting the use of loss-reducing technologies, could increase fish supply and food security in small-scale fisheries.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".