Assessment of Forest Products’ Utilization among Rural Dwellers in Osun State, Nigeria
Bibliographic record
Abstract
This study assessed the level of utilization of forest products by rural dwellers in Osun State Nigeria. It specifically described the socio-economic characteristics of the rural dwellers involved in utilization of forest products and determined the level of utilization of the forest products. Multistage sampling procedure was used in selecting a total of 240 respondents from four local government areas. Structured interview schedule and Focus Group Discussion (FGD) guide were used to collect quantitative and qualitative data respectively. Data collected were analysed using descriptive tools such as mean, frequency counts, percentages and standard deviation; and inferential tools such as Chi-square and Correlation analyses, as well as content analysis for qualitative data. The mean age of the respondents was 47.08±11.67 years, while the mean total annual income was ₦360,012.71±₦3000.59 respectively. Furthermore the mean level of utilization of forest products was 257.99±71.66. The type of organization ( χ 2 = 128.693), ethnicity ( χ 2 = 42.616) and the major occupation ( χ 2 = 13.882) were associated with level of utilization of forest products at p ≤ 0.01 and p ≤ 0.05. Age (r = 0.172) and total income (r = 0.222) significantly correlated with level of utilization of forest products at p ≤ 0.01. In conclusion, there was a moderate level of utilization of forest products in the study area. It was therefore, recommended that specific forest policies be put in place to ease access, control and maximum use of forest products.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".