Assessing the economic impacts of post-harvest fisheries losses in Malawi
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents the findings from a qualitative and quantitative fisheries value chain and post-harvest loss study conducted in four Malawian lakes: Malawi, Malombe, Chilwa and Chiuta. The research found that the estimated total value of the fisheries value chain was US $454 million in 2016 – or 7.2% of the projected 2017 GDP. This is over 2.5 times the previously reported (Government of Malawi, 2017) beach landing site value. The study found that 43%, 54% and 69% of fish have physical and quality losses at the beach, processing and marketing nodes, respectively. However, high quality loss is not proportionately affecting economic loss. The fisheries value chain experiences less than 10% annual economic losses mainly due to low pricing sensitivity of existing products. The overall economic losses are nine percent, being highest at the beach node (19.3%) and smallest at the market node (2.1%). The main reason for this unusual relationship is that pricing is not sensitive to quality, which means that almost all types of quality of product is sold resulting in a recorded relatively low overall physical loss of 4.1%. An important conclusion of the assessment is that even though the economic losses are relatively modest in relation to the total value, the quality losses, which range between 43% and 69% depending on the node, indicate that the potential for health impacts and nutritional value loss are high throughout the value chain. Value chain improvements are recommended to provide economic and nutritional benefits for end-users and value chain actors.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it