A Public Sector Comparator (PSC) for Value for Money (VFM) Assessment Tools
Bibliographic record
Abstract
In a generic sense, when procuring Public Private Partnership (PPP) projects, value for money (VFM) assessment could be determined through a comparative analysis of contractors’ proposals against Public Sector Comparator (PSC) documentation. A PSC is a hypothetical framework used as a procurement strategy tool in evaluating VFM and has been a trademark for most countries across the globe such as UK, Australia, Hong Kong and Canada. However, this strategy has not been systematically formulated and applied in Malaysia. The probable reasons for this predicament could be due to the controversy in risk calculation; lacking of non- financial aspects and future cash flow, inappropriate discounted rate used and the difficulty in the PSC calculation. Hence, the aim of this study is to ascertain a complete PSC framework for PPP projects embracing financial and non-financial aspects across project phases (i.e., strategy formulation; procurement; construction and operation phase). The empirical research via questionnaire survey was conducted among PPP stakeholders. The results indicated that the development of PSC framework would facilitate a comprehensive dimension of VFM evaluation for PPP projects in Malaysia.
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.
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 | 0.034 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".