SOCIO-ECONOMIC FACTORS AFFECTING THE MARKETING OF GARRI IN PORT HARCOURT CITY OF RIVERS STATE
Bibliographic record
Abstract
This study assessed the socio-economic factors affecting the marketing of garri in Port Harcourt city of Rivers State. Specifically it examined the organization of garri marketing in Port Harcourt; the marketing cost and margin and recommended ways of improving the marketing system of garri in the study area. Four major markets (Mile one market, Mile three market, Oil mill market and Creek market) in Port Harcourt city were purposivefully selected for this study out of which 20 respondents were randomly selected from each of the market giving a total of 80 respondents. Interview schedule was used to elicit information from the respondents. Data collected include socio economic characteristic of the traders, purchases and sales transaction, marketing cost and returns and finally problems encountered by the traders. Analysis of the data obtained showed that majority (83%) of the respondents were female, 78% had basic education which help them in keeping proper record and in performing marketing function effectively. Also 87.5% had been in the business for between one to ten years while 75% were retail traders. Furthermore 50% of the garri traders buy their product from outside Port Harcourt while the major means of transportation used by the traders was taxi (40%). Some of market information was mainly from colleagues (61%). The total average marketing cost per ton is N2372.9. The respondents earn fairly high net returns in relation to the marketing function they perform but cannot be said to be excessive. The major problems encountered in the marketing of garri in the study area include transportation problem and lack of capital. It was therefore recommended that micro credit facilities through formal financial institution should be made accessible to the traders so as to help them acquire sufficient capital for the transaction. Also marketers can pool their resources together to find a lasting solution to the problem of transportation by either buying a bus or hiring one to convey them to and fro at cheaper rates and more convenient situation.
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How this classification was reachedexpand
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 | 0.004 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".