Determinants of Inlet Choices of Sheep and Goats Traders in Ghana: A Case Study of Kumasi and Tamale
Bibliographic record
Abstract
The consumption of Small ruminants’ meat (sheep and goat) form an integral component of an average Ghanaian’s diet due to the country’s emergence to a middle income earning status and awareness of the outstanding benefits of a nutritious diet. However, periodic shocks in market availability of products severely impacts access and in most cases cause price hike hence affecting dietary patterns especially of urban dwellers that are mostly habitual in recurrent consumption of these products. Against such insight, this study investigates the factors that influence the inlet choice of sheep and goats traders in two urban towns of Ghana (Kumasi and Tamale) using Multinomial logit model. The selection of Kumasi and Tamale markets were based on the progressive marketing and consumption of sheep (S) and goats (G). A multi stage sampling technique was used in this study. A reconnaissance survey was carried out in Kumasi and Tamale metropolis in order to identify the existing markets and to generate the sample frame. Ten markets were purposively selected based on the proximity of the markets to the urban centres. A total of 284 traders were randomly sampled from the sampling frame. A structured questionnaire was used to collect relevant data, and analyzed using descriptive statistics and inferential statistics. The results of the study showed that majority of the traders were male with most (43%) falling within the age range of 31 to 40 years. International market (Burkina Faso) was identified as the major source of sheep and goats with 37% of total respondents sourcing from there. The major factors found to be influencing the inlet choice of sheep and goats by sampled traders in the study areas were prices, licenses, quantity of animals handled, transportation cost, education, experience and age of the traders. Based on the findings of the study, the following recommendations were suggested to improve the SG trade in urban centres of Ghana: Credit provision to traders who desire it to establish and expand their investment; reduction of license cost; and general reduction of fuel price so as to ease economic mobility of products.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".