Assessment of Sustainable Livelihood Assets of Farming Households in Akwa Ibom State, Nigeria
Bibliographic record
Abstract
There is surfeit of evidence on increase poverty and low agricultural productivity among majority of rural dwellers in Nigeria. Researches have established an inverse linked between rural poverty and sustainable households’ asset based. Agricultural production, being the major livelihood source for majority of rural dwellers needs considerable asset or capital for it to be considered as sustainable. Based on this assertion, the study assesses the sustainable livelihood assets of farming households in Abak Local Government area of Akwa Ibom state in Southern region of Nigeria. A multi-stage sampling technique was employed to select 110 farming household heads in the study area. Structured questionnaires were used to collect cross sectional data from respondents. Descriptive tools were used to analyse data collected. The socioeconomic features of respondents revealed a sample population that is fast ageing, dominated by married male and moderately educated. Result also showed that, respondents had considerable piles of physical, social and natural assets to assist in livelihood sustenance. However, the index of capacity structure of sustainable livelihood assets revealed a huge deficiency in financial and human assets among farming households in the region. Hence, it is recommended that, farming households should increase their human assets by encouraging education of the younger household members. Also, efforts should be made to improve social capital formation among farming households and communities.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".