Building Failure and Collapse in Nigeria: the Influence of the Informal Sector
Bibliographic record
Abstract
The occurrence of building failure and collapse has become a major issue of concern in the development of this nation as the magnitudes of this incident are becoming very alarming. This paper therefore examines the incidents of building failure/collapse in Nigeria. By focusing on six major states from each of the six geo-political regions of the country, the paper examined the contributory role of the informal sector to this decadence. The study indicated that the building failure and collapse stem principally from hasty construction, low quality workmanship, poor supervision, inexperience (use of incompetent hands), ignorance, evasion/ non-compliance with building regulations and non enforcement of building quality, standard and control on construction site/market. This study has revealed that more than 70% of the reported cases of building collapse in Nigeria stemmed from the informal sector. It further showed that 70-0%, 23-3% and 6.7% of the reported cases occurred in private, public and corporate organizations respectively. In this paper, it is concluded that it is important to educate or giver further advice to the government and the governmental agencies to be proactive to their duties in order to curb/reduce this negative image.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".