Subcontractor Oversight on Construction Cost Overruns in Real Estate Projects in Nairobi and Kisumu Counties, Kenya
Bibliographic record
Abstract
Cost overruns have provided a significant challenge in the construction industries of both developed and developing countries. The purpose of this study was to investigate the influence Subcontractor Oversight on Construction Cost Overruns of real estate projects in Nairobi and Kisumu Counties. The study was guided by objective, to establish the extent to which subcontractor oversight influence real estate construction projects cost overruns. The research adopted descriptive survey and correlational research designs. The study targeted a population of 4000 project professionals that constituted 7 professionals from active real estates in Nairobi and Kisumu Counties and 10 key informants from the real estate industry. Using the Krejcie and Morgan table of sample size determination, the sample size for this study was 351. The study then adopted stratified, simple random and purposive sampling methods to select appropriate sample sizes from the study population strata. Structured questionnaire was the main instrument for data collection, supported by interview guide. Hypothesis was tested at α=0.05 level of significance and the results were: H0: There is no significant relationship between subcontractor oversight and real estate construction projects cost overruns was rejected since P=0.000<0.05. Considering the study findings and conclusions, the following recommendations were made: Project professionals and other relevant real estate project stakeholders should encourage comprehensive subcontractor oversight as critical concerns in assembling pertinent information and creating avenues that could be utilized to reduce real estate construction projects cost overruns.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".