INFLUENCE OF SOCIO-ECONOMIC VARIABLES ON FOOD SECURITY STATUS OF FARMING HOUSEHOLDS IN WUKARI, TARABA STATE, NIGERIA
Bibliographic record
Abstract
This study examined the Influence of Socioeconomic Variables on Food Security Status of Farming Households in Wukari, Taraba State, Nigeria. A multi-stage sampling technique was used to randomly select 112 farming households for the study. Data were collected on socioeconomic characteristics, food consumption pattern and causes of food insecurity using a structured questionnaire. Data were analyzed using descriptive statistics, food security index (FSI) and binary logistic regression model. The summary statistics of food security indices revealed that majority (56.2%) of the selected households were food insecure, and out of the households that experienced food shortage incidence, majority (51.6%) indicated that food shortage was mostly experienced in the third quarter of the year (July- September). The regression analysis on the influence of socioeconomic characteristics on food security status among farming households indicated that household size (P<0.01), education level (P<0.05) and household income (P<0.05) were statistically significant with 0.623, 2.471 and 1.044 exponential values respectively. High food prices; conflict and civil unrest; and high cost of transportation were ranked the topmost causes of food insecurity in the study area. The study concluded that majority of the selected households were food insecure with the incidence of food insecurity more concentrated in the third quarter of the year (July – September). And that food security status was significantly influenced by the socioeconomic characteristics of the household heads with high food prices; conflict and civil unrest; and high cost of transportation as the topmost causes of food insecurity in the study area. It was recommended that facilitating access to adequate and affordable food; affordable means of transportation; and taking into consideration by policy makers, the significant socioeconomic factors influencing food security will improve the food security status of farming households in the study 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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".