DETAILING THE SOCIAL CONTEXT OF INEQUALITY IN THE RURAL AREAS OF EDO AND DELTA STATES OF SOUTHERN NIGERIA
Bibliographic record
Abstract
Inequality in societies is a broad-based theme, which is subject to various socio-demographic contextualisations apart from gender. This study considers social inequalities in the rural areas of Delta and Edo States of Southern Nigeria, in the context of four social markers, which are gender, age, marital status and educational qualification. The study was carried out using 3,188 questionnaire copies administered in six (6) Local Government Areas (LGAs) of both states. The results showed that although income did not vary significantly between males and females, F (1, 3186) = 0.915, p = 0.339, males had a higher mean income scale compared to females. This was observed despite women having significantly less input into decisions concerning farm and non-farm economic activities (p = 0.00). Women also reported higher working hours in the primary sector only (p = 0.00) and fewer sleep hours daily (p = 0.69). In terms of age, middle-aged adults (40 – 59 years) and the elderly (60 years and above) had higher monthly income scales (p = 0.00), higher levels of input into decisions on farming activities (p = 0.00) and spent more hours in unpaid productive work on a typical day (p = 0.11) compared to the younger age groups. However, the younger adults (20 - 39 years) spent the most hours working on a typical day (p = 0.00). In terms of marital status, married adults had the highest scale of monthly income (p = 0.00), perhaps due to support from their spouses. Widowed and separated/divorced adults had more input into decisions on household farming activities (p = 0.00). For inputs into decisions on non-farming economic activities and the use of income from non-farming activities, single adults had the highest scale (p = 0.00). Also, adults with no formal education and those with primary educational qualifications had a higher scale for monthly income and higher levels of input into decisions on farming economic activities (p = 0.00), compared with those with secondary and tertiary educational qualifications. However, those with secondary and tertiary educational qualifications had more input into decisions on non-farming economic activities. The study recommends the re-orientation of family and community values to support the vulnerable groups in society, such as women, youths, the elderly and the widowed. It also recommends that indigenous skills aimed at sustaining livelihood be acquired irrespective of the educational qualification attained.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".