Ensuring operational performance for promoting sustainable practices in Public Private Partnership (PPP) projects in the UK
Bibliographic record
Abstract
A great deal of focus has been placed by different governments on their construction industries as it is known for being a large polluter of the planet, as well as being a huge consumer of energy and emitter of carbon. The research was completed by investigating major PPP themes related to UK (namely, challenges to UK construction, operational performance of PPP projects, and drivers of PPP projects). To ensure enough participants were reached, the snowball sampling technique was used to collect data from 156 industrial professionals. Relative importance index (RII) analysis was performed to check the ranking of the factors, and to determine the significance of each factor. This analysis revealed a large significance on time and cost management issues within the challenges to UK construction section. Sustainability presented highly significant results relating to modern methods of construction like BIM as well as the use of modern schemes such as the PF2 (Private Finance 2) scheme. This was also found to be an important factor in the operational performance of PPP projects as well as resolving financial and fiscal issues within the public sector. This research can support public and private sectors to develop advanced collaborative networks to boost productivity.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.005 |
| 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".