Determinants of Unethical Performance in Nigerian Construction Industry
Bibliographic record
Abstract
Unethical performance is an impediment for economic development and good governance. It is more than true to say it is the bane of development in developing nations of the world under which Nigeria is categorized. The construction sector was highlighted for its practices in areas such as bribery, environmental destruction, capital flight, dangerous practices, poor quality, whistle blowing, etc. and it is also associated with unethical behaviour through the entire construction life cycle. This paper therefore, x-rayed the determinants of unethical performance in Nigerian construction industry with a view to identifying the causes in all the stages of building project. Well structured questionnaires were self administered to direct stakeholder involved in the execution of building projects considered for this research work. Literature review revealed that corruption is evident in the construction industry which causes a lot of setback to project such as abandonment of such project and if complete may be completed below standard. Data were presented and analyzed using non parametric statistic; tools used were Severity indices and Relative importance index rather than mean scores since the data were ordinal in nature. It was observed that the building construction industry is perceived to be more susceptible to ethical problems because of several features and that corruption has effect on all stages of construction right from Planning, Tender stage to Completion stage. It was recommended that viable legislation as a mechanism to deal with small levels of corruption by strengthening professional institutions that will punish erring members will prevent corruption to a certain degree; and enforcement and monitoring measures of anti corruption agencies is believed can enhance transparency, accountability and reduced unethical behavior, and this will create an enabling environment possible for the industry to thrive, operate, and improve on quality and quantity of infrastructure on a more sustainable basis and thereby foster good construction practice ethics.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".