Public–private partnerships: critical factors for procurement of capital projects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Looking for innovative approaches towards procurement of projects through public–private partnerships has become more common in the public sector which has the largest capital project spending. It is used to improve efficiency in the procurement of projects and get more value for money. The critical success factors in public–private partnerships for procurement of capital projects identify factors contributing to the successful procurement of capital projects which is seen as one of the many management practices that contribute to corporate success. A model based on an analytical hierarchy process was developed to investigate the critical success factors using information from owners, project managers, consultants/contractors, financiers and operators worldwide for procurement of capital projects. Owner satisfaction with the delivered project, clearly defined project mission, objective and scope definitions, adequacy of plans and specifications, lack of legal encumbrances, and appropriate funding mechanisms were shown to be the topmost of the success factors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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