Published by Canadian Center of Science and Education 127 Tackling Causes of Frequent Building Collapse in Nigeria
Bibliographic record
Abstract
A building, once properly constructed is expected to be in use for a very long time. Although every society has its own problems and Nigeria is not an exception yet the very recent challenges of buildings collapsing in various locations have been giving the various arms of government and the people of Nigeria sleepless nights in view of the enormous loss of huge investments in housing, properties and human life. The major challenge on the issue of building collapse is that individuals differ radically from one another on the professional to blame as the major cause of the collapse of a building. This study reviews current challenges in the building industry in relation to collapse of buildings, loss of lives and properties. Data for the study were obtained through structured questionnaires administered to landlords and professionals in the construction industry in addition to academia in the built environment. Historical data of past collapsed buildings in Nigeria were also discussed. Findings from the three prominent groups were varied. First, building experts blamed building collapses on the use of low quality building materials coupled with employment of incompetent artisans and weak supervision of workmen on site. Second, public opinion revealed that the blames of building collapse were due to non-compliance with specifications/standards, use of substandard building materials and equipments and the employment of incompetent contractors. Third, opinion of the academia on remote causes of building collapse showed that the
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.498 | 0.134 |
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".