Small and Medium-Scale Agro-Produce Entrepreneurship and Promotion of Non-Oil Exports from Nigeria
Bibliographic record
Abstract
The study undertook a critical appraisal of marketing communications’ tools for the promotion of non-oil exports by small and medium-scale agro-produce entrepreneurs in Nigeria. It was motivated by the problem of low performance of the non-oil export sub-sector in the country. This is in spite of government’s incentives and the introduction of African Growth and Opportunity Act (AGOA) in May 2000 by the United States of America, which was to improve export of products particularly in agriculture from Sub-Saharan Africa to the United States. The focus of AGOA was to enhance the economic growth of countries in Sub-Saharan Africa through export leverages rooted in the reduction of tariffs and non-tariff barriers. The specific objectives of the study were to: determine the effect of internet-marketing communications media as tools for promoting small and medium-scale farming entrepreneurs’ non-oil exports from Nigeria; and ascertain the effect of social-media communications as tools for promoting small and medium-scale farming entrepreneurs’ non-oil exports from Nigeria. Survey research design was adopted in the study. The sample frame comprised small and medium-scale agro-produce farmers drawn from three States representing the three-former regional structure of the country. Data sourced were statistically analyzed. Results indicate that both internet marketing and social media communications were not yet significant for promoting non-oil exports by small and medium-scale farming entrepreneurs from Nigeria, because of other serious diluting variables like poor products’ quality, packaging, processing, storage and other systemic bottlenecks. It was then recommended that the Governments in sub-Saharan Africa, particularly Nigeria, should introduce and educate small and medium-scale agro-produce entrepreneurs wishing to go into non-oil exports, among other things, to improve their products’ qualities to meet international markets’ standards, before employing internet and social media marketing communications to create global awareness and demands for them.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".