Bibliographic record
Abstract
This study assessed the effect of ICT on rice marketing in Jalingo Local Government Area of Taraba State, Nigeria.The specific objectives were to describe the socio-economic characteristics of the respondents, investigate the level of usage of ICT on rice marketing, determine impact of ICT on rice marketing and identify the constraints associated with the use of ICT in the study area.Primary data were collected from 100 respondents using purposive and simple random sampling techniques.Results were analyzed using descriptive and inferential statistics.The analysis revealed that, majority 67% were female, 54% were married and 68% attended one form of education or the other.Most (59%) of the respondents had a monthly income earnings of less than N20,000 per month.The result on level of usage of ICT showed that ICT help the marketers to know market days (3.0), know market where products can be sold (2.9), know the market to attend (3.0) know type of products to sell (2.55), and know different market location (2.8) while the cut-off point of the mean was (2.5), indicated that ICT have improved the knowledge of rice marketers on marketing information.The result of T-test analysis shows that the mean (x) of income and volume of sales (601) was higher after the use of ICT compared with the mean (x) of income and volume of sales (4.59) before the use of ICT facilities.This signified that ICT have positive effect on rice marketing in the study area.High call tariff, erratic power supply, fluctuation of service and no network coverage were the major constraints faced by the respondents.It was recommended that the National Communication Commission (NCC) as a regulatory body should ensure that GSM service providers act within the ambit of the law by not charging exorbitant tariff on consumers and tackle the problem of fluctuation of service and no network coverage.Government on the other hand should endeavour to tackle the problems of erratic power supply to facilitate the use of ICT tools for rice marketing information in the area.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.932 | 0.918 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".