Development of a Performance Assessment Model for Contractors in Saudi Arabian Construction Projects
Bibliographic record
Abstract
Saudi Arabia leads the Gulf Cooperation Council countries in the construction industry, with 63% of the region’s future projects and $1.4 trillion in building and transportation projects. Previous studies have shown that 60% of construction projects are behind the schedule. One of the reasons behind this delay is the inappropriate selection of contractors for construction projects; therefore, the development of a contractor performance assessment model is needed for construction projects in Saudi Arabia. The proposed model includes the major key performance indicators (KPIs) used to measure contractors’ performance in construction projects. Afterward, a questionnaire is conducted with construction professionals to recommend the important KPIs to consider while developing the model. The analytic hierarchy process decision‐making technique is applied for assessing relative priorities among the identified KPIs based on expert or decision‐maker consensus. The results show that the time category is the most important in selecting the best contractor, emphasizing the significance of timely project completion.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".