Bibliographic record
Abstract
Abstract: Much of the public debate about public‐private partnerships (P3s) has occurred through the lens of those who either oppose or support this increasingly popular method of delivering public infrastructure assets. Despite some scepticism in the academic literature, an analysis of the key arguments for and against P3s concludes that the P3 model can successfully deliver public infrastructure goods and services, provided that certain key thresholds are met. Lessons learned from early experiments in P3s and from the experience of the newer government P3 procurement agencies suggest that P3s can provide value for money if risk is allocated to the party best able to manage it. An appropriate risk allocation requires that governments have the expertise to identify all of the relevant risks before entering into the partnership contract. Governments must also have the contract management skills to ensure that those risks are in fact borne by the private sector. To maintain public confidence in the P3 model, governments must live up to their own obligations of transparency and accountability and not succumb to private‐sector demands for confidentiality. The article recognizes that not all government goods and services can meet the threshold but that, if they do, it argues strongly for the efficiency and effectiveness of the P3 model. Sommaire: Une grande partie du débat public au sujet des partenariats entre le secteur public et le secteur privé (les P3) a eu lieu par l'entremise de ceux qui opposent ou soutiennent cette méthode de plus en plus populaire de livrer de l'infrastructure publique. En dépit d'un certain scepticisme émanant des documents d'universitaires, une analyse des principaux arguments en faveur des P3 et contre ceux‐ci conclut que les P3 peuvent livrer avec succès des produits et services d'infrastructure publique, à condition que certains seuils clés soient atteints. Les enseignements tirés des premières expériences de P3 et de l'expérience des plus récents organismes d'approvisionnement gouvernementaux P3 laissent entendre que les P3 peuvent apporter une optimisation des ressources si le risque est attribuéà la partie la plus apte à le gérer. Une bonne répartition du risque exige que les gouvernements aient l'expertise pour identifier tous les risques pertinents avant de signer le contrat de partenariat. Les gouvernements doivent aussi avoir les compétences en gestion de contrats nécessaires pour veiller à ce que les risques soient en fait assumés par le secteur privé. Pour maintenir la confiance du public dans le modèle de P3, les gouvernements doivent respecter leurs engagements de transparence et de reddition de comptes et ne pas céder aux exigences du secteur privé concernant la protection des renseignements personnels. L'article reconnaît que tous les produits et services gouvernementaux ne peuvent pas tous atteindre le seuil de conformités mais que, lorsqu'ils y parviennent, le modèle de P3 est alors hautement efficace et efficient.
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 | 0.025 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.030 |
| Scholarly communication | 0.020 | 0.026 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.014 | 0.014 |
| Insufficient payload (model declined to judge) | 0.015 | 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".