Assessment of Information Needs of Shea Butter Processors’ on Modern Processing Technologies in North Central Agro-Ecological Zone of Nigeria
Bibliographic record
Abstract
To ensure sustainable shea butter production in North Central Agro-ecological zone of Nigeria using modern shea butter processing technologies necessitated this study. The major objective of the study was to identify the information needs of shea butter processors’ on modern shea butter processing technologies whilst examining the respondents’ socio-economic characteristics, ascertaining their awareness, areas of information needs, sources of information, and perception on the effect of inadequate information on modern processing technologies were the specific objectives. Primary data were collected from 216 processors’ using multi-stage sampling procedure. The data were analysed using descriptive and inferential statistics. Findings revealed that most (90.3%) of the processors’ were females, young and married with little or no formal education and having between 6 to 15 years of processing experience. Respondents obtained information mostly from fellow processors ( =70) and cooperative societies ( = 67). Perceived areas of information need include kneading ( = 89), crushing ( = 88), roasting ( = 85) and milling ( = 84). Perceived effects of inadequate information on modern processing technologies were low yield of shea butter ( = 78), low income ( = 76), and local use of local technologies ( = 71) and poor packaging of shea butter ( = 78). Respondents’ sex (χ2 = 22.076, 0.000), educational level (χ2 = 86.983, 0.000) and years of processing experience (χ2 = 22.076, 0.000) had significant association with their perception of information needs. Creation of awareness on modern shea butter processing technologies through the use of more radio programmes aired at appropriate time and the use of leaflets produced both in English and local languages is recommended.
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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 | 0.005 |
| 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 | 0.000 |
| 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".