Achieving Canadian Housing Project Success through Artificial Intelligence, Risk Monitoring and Controls
Bibliographic record
Abstract
This study aims to examine the role of internal project risks and project risk monitoring and controls towards the success of housing projects in St. John, Newfoundland and Labrador, Canada. The sample was drawn from the project team members involved in the housing projects in St. John, Newfoundland and Labrador, Canada. The data were collected from 246 participants using a purposive sampling technique. Data was analysed by employing the PLS-SEM method. The results revealed that project risk has a significant negative effect on both project success and project risk monitoring and control. However, project risk monitoring control insignificantly mediates the impact of project risks on project success, whereas project risk monitoring control has a positive and insignificant influence on project success. PRM principles and guidelines insignificantly moderate the effect of project risks on project success. Likewise, awareness of AI in project risk analysis insignificantly moderates this effect. The study recommends that the project managers should prioritise the identification and mitigation of internal project risks to minimise their adverse impact on project outcomes.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".