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 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".