A Comparative Analysis and Evaluation of Specialist PPP Units’ Methodologies for Conducting Value for Money Appraisals
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
Governments throughout the world are turning to public‒private partnerships (PPPs) as a means of providing new infrastructure. The decision to adopt a PPP over conventional government procurement is usually based on a value for money (VfM) appraisal, but this analysis is conducted differently in different countries. This article describes the correct way to conduct VfM analysis if the goal is to minimize the present value of the costs to the Treasury and if the goal is to maximize social welfare. It then compares the documented methodologies of nine specialist PPP units. It identifies four ways in which these methodologies depart from either of the correct approaches, and shows how each departure favors the PPP option. Finally, it shows how the UK approach might be augmented to determine the best value to society.
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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 | 0.029 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 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