Causes and Effects of Cost Overrun on Construction Project in Bahrain: Part 2 (PLS-SEM Path Modelling)
Why this work is in the frame
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Bibliographic record
Abstract
In part 1 of the paper, cost overrun factors associated with the construction industry in Bahrain were identified, and risk maps were developed based on a survey and actual construction projects records. This part of the paper adopted structural equation modelling (SEM) approach to assess the effects of cost overrun factors on project cost in Bahrain. SEM is the graphical equivalent of a mathematical representation to study relationship between dependent variable to explanatory variable. SEM is regarded as extension of standardized regression modelling and is important tool to estimate the causal relationship between factors. The collected data from the questionnaire and actual projects of part 1 were modelled and analyzed using SmartPLS v3.0 software. Results showed that approximately 60% of cost overrun was influenced with the factors identified in part 1. The Global fit index (GoF) value of the developed model was 0.591, indicating that the model has enough power in explaining the relationship between identified factors and cost overrun.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it