Determinants for Choice of Fish Market Channels: The Case of Busia (Uganda/Kenya) Border
Bibliographic record
Abstract
The study investigated factors influencing the choice of fish traders’ marketing channel by fish trader and the determinants of their gross margins. A multistage sampling technique of 115 fish traders was used. Four key informants and two focus groups participated in the study. A Probit model was used to determine factors that influenced the choice of fish traders’ marketing channel. Findings from the study revealed that volumes of fish traded per month, distance to market, membership to a fish marketing organization, payment mode, household size, presence of other sources of income were statistically significant in determining traders’ choice of marketing channel. In addition, it was found that a formal trader on average sold 6.882 tons of fish whereas 2.095 tons of fish were sold by the informal trader per month. The study therefore recommends that Informal traders be clustered into a marketing organisation; through which they can be trained on business diversification, collective marketing, and group savings to ease access to credit. It is important to eliminate the challenges encouraging informal cross border fish trade by harmonizing regional fish standards, facilitating exchange and use of fishery trade information. Further, there is need to strengthen the data collection systems in order to have accurate estimates on the contribution of fish to trade and food security.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".