Understanding the Livelihood Strategies of Informal Settlers in Enugu Urban, Nigeria
Bibliographic record
Abstract
Although the governance structure and environmental and agricultural impacts of peri-urban development in sub-Saharan Africa are known, little research attention has been given to understanding how residents of such areas are coping with their livelihoods in challenging locations in many urban centers in this region.This research aimed at investigating the livelihood coping strategies of peri-urban dwellers in Enugu, Southeast Nigeria.A crosssection survey involving two-stage cluster sampling technique was used in selecting 816 registered household heads who participated in this study.Questionnaire method was employed in the collection of data.Principal Component Analyses and multiple linear regression statistical tools were used to analyze the data.The results revealed the four main coping strategies to include home-based business (49.95%),retail trading (16.50%), domestic working (16.34%), and street trading (12.03%).Factors that influenced the identified coping strategies of these dwellers include age, sex, take-off capital, type of housing unit, and space availability.This study implies that contemporary urban planning strategies should take cognizance and integrate these coping strategies into modern urban planning practices and policy making.This is vital in ensuring that the contributions of this category of urban residents to the gross domestic product are adequately harnessed for national development.
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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 | 0.001 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".