Analysis of Price Variation in the Marketing of Garri in Delta State, Nigeria
Bibliographic record
Abstract
The study examined price variation in the marketing of garri in Delta State Nigeria. A multi-stage sampling procedure was used in drawing up a sample of 180 garri marketers from six purposively selected major garri markets each from six purposively selected Local government areas. Data collected with the aid of questionnaire were analysed using both descriptive and inferential statistics. The results revealed that majority of the marketers were females that are still in their economically active age group and relatively literate. Majority of the marketers were middlemen who sold mostly to their fellow middlemen. Most of the respondents agreed that marketing cost is the major cause of price variation in garri market while season of the year was the most notable problem facing garri marketers in the study area. Also, the result of Analysis of Variance revealed that there were significant differences in garri prices among the six markets. It was concluded that in order to stabilize price of garri and income of garri marketers, in order to ensure sustainable food security in Delta State, measures should be taken to provide adequate transportation and establish storage facilities. This will invariably cut down marketing costs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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 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".