USING MULTI-CRITERIA DECISION MAKING TO DETERMINE THE CRITICAL SUCCESS FACTORS FOR PROCUREMENT OF CAPITAL PROJECTS UNDER PUBLIC-PRIVATE PARTNERSHIPS
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
The investigation on project success has attracted the interest of many researches and practitioners. Determining the critical success factors for procurements of capital projects are contemporary phenomena. This paper presents the outcome of an investigation into the Critical Success Factors in Public-Private-Partnerships (P-P-P) for procurement of capital projects using Multi Criteria Decision Making process. Drawing on the results of responses from a survey of 705 experts involved in P -P-P projects worldwide, the paper presents the Critical Success Factors (CSF) from a list of 47 success factors identified as contributing to the successful delivery of capital project. The study revealed that owner satisfaction with the delivered project, adherence to schedules/budget/quality/ safety/environmental controls and appropriate funding mechanisms were predictable while lack of legal encumbrances, clearly defined project mission and adequate planning and control techniques were less commonly expected.
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 | 0.002 | 0.004 |
| 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 | 0.001 | 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