Infrastructure Provision and Private Lands Acquisition Grievances: Social Benefits and Private Costs
Bibliographic record
Abstract
Urban infrastructure delivery will benefit a nation directly by improving public welfare. In urban areas, it is particularly necessary for sustainability. A crucial prerequisite for this is the provision of land. However, suitable lands for specific projects may not be available, and where such lands exist they may be in private holding. In Nigeria the Land Use Decree of 1978 provides that such properties can be acquired compulsorily by governments with powers of eminent domain for overriding public interest. This decree is however silent on the issues of injurious affection and disturbance. Currently in Lagos, the initiatives of the government to improve public welfare have been geared greatly towards solving the problems associated with urban and infrastructure decay. Series of public developments like road expansions have been embarked upon, thereby disturbing several land owners. This paper aims at demonstrating the adequacy of compulsory compensation policy on injuriously affected land owners within Victoria Island, Lagos. The study also makes corresponding clarifications with acquiring authorities, on a study population within a prestigious socio-economic status. Data collection was based on a survey which adopted the use of questionnaires, and structured interviews. Results revealed the opinions of various stakeholders in the process and deficiencies in the process amongst others. This study will serve as a guide for urban land management planning and 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".