Socio-Economic Characteristics and Livelihood Assets of Wetlands Users at Ede Region, Southwestern Nigeria
Bibliographic record
Abstract
The study examines the socio-economic characteristics of the users of wetlands, the relationship between their status and their resources with a view to land reform in the region. The study employed primary and secondary data. Primary data explored 566 structured questionnaires administered on wetland users using the snow-ball method soliciting information on: respondents’ indicators of livelihood assets, resources, human capital, socio-economic characteristics, quality of dwelling, sanitation and ownership of land. Secondary data was sourced from conventional sources. Data was analysed using descriptive and inferential statistics. Results show that over 70% of respondents were above 41 years of age and were predominantly small scale food-farmers. Furthermore, 59.4% of respondents lived in Brazilian type of houses “face me I face you” with 49.0% of the houses in faire state that need maintenance, 60.3% had bare ground floors while 44.3% were personal houses and 31.7% family houses. Similarly, it was established that the depth of poverty in relation to landed assets showed that 58.6% of the rich compared to 20.7% of the moderate poor and 20.7% of the poorest ranked households owned more than 10 ha of land. The implications of this is that a greater proportion of productive assets (Land) in Ede region were in the hands of the non-poor ranked households which has continued to widen the gap between the rich and the poor and if poverty has to be tackled, then there must be a way forward through “land reform” to make this very important livelihood asset available to the extreme poor.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.014 |
| 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".