Slum Housing Conditions and Eradication Practices in Some Selected Nigerian Cities
Bibliographic record
Abstract
This paper reviews the state of urban slums in Nigeria and attempts to explicate the issues that arise from the approach of slum eradication in some selected cities (Lagos, Port Harcourt, Abuja and Jos). A wide range of secondary source material was collected and analysed thematically. The analysis affirms that the slumming process in the four cities is significantly accounted for, by rising urbanisation. In Lagos and Port Harcourt cities the urbanisation appears to be linked to the industrialisation process but this is not the case in Abuja and Jos. However, some factors were found to be common in the slumming process of the four cities such as: One, the rising population which is increasing the demand for urban services. Two, there is acute shortage in the supply of adequate housing for the low-come and poor households. Lastly, there is inadequate arrangement for the effective management of urban growth and expansion. Other issues identified are: the absence of mechanisms for the prevention of slum formation; a preference for the demolition of slums by authorities as opposed to their improvement; a wide practice of implementing eviction on short notice; and in most instances, government authorities have failed to provide adequate alternative shelter to evicted households. These findings clearly indicate that the subsisting housing and urban development policies leave gaps for such flawed practices. Accordingly, policy recommendations and suggestions for empirical study are made.
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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 | 0.002 |
| 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) | 0.001 | 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".