Measuring value for money in public private partnerships: a review of the public sector comparator in Australia, Canada, New Zealand and United Kingdom
Bibliographic record
Abstract
Public-Private Partnerships are defined by the OCDE (2008) as "an agreement between the government and one or more private partners (which may include the operators and the financers) according to which the private partners deliver the service in such a manner that the service delivery objectives of the government are aligned with the profit objectives of the private partners and where the effectiveness of the alignment depends on a sufficient transfer of risk to the private partners". Due to their characteristics and due to governments' limited ability of funding public infrastructures projects, they are becoming part of the government's portfolio as a good solution to fill the "infrastructure gap". We undertake OECD (2008) assembly of the top ten countries with the largest public-private partnerships deals in 2003 and 2004 and, we select those countries that have more formalised policy regarding the use of the public sector comparator and those that are more quoted in the literature (Australia, Canada and United Kingdom). We also select New Zealand because they already adopt the public sector comparator methodology even thought there percentage of public-private partnerships projects is not so expressive in the public investment as the other countries that we analysed. The purpose of this work is to find which methodologies are implemented, while establishing a comparison between all four countries. Several conclusions arise from our investigation. We found that all four countries adopt the public sector comparator in order to assess bids, and it is always created in the early phases of the project plan. We also observe that they all use different methodologies with the same purpose: achieving value for the taxpayers. However, there are substantial differences on the methodologies developed in each country.
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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 | 0.045 | 0.124 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.032 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".