A Re-examination of the Public-Private-Partnership Discourse: Was PPP the way to Upgrade London Underground’s Infrastructure?
Bibliographic record
Abstract
Public-Private-Partnerships (PPPs), known as 3Ps in the US and Canada, were introduced in to the United Kingdom (UK) by the Conservative government in their 1992. Since then, PPPs in the UK accounted for over 57 billion pounds worth of capital expenditure. The Transport sector, whilst not having signed the most PPP deals, accounted for the largest capital expenditure of all sectors; approximately 12 billion pounds of which urban transit PPPs has accounted for approximately 7 billion pounds. When compared with Europe, US and Canada, the UK has led the way with PPP financing however this model is becoming increasingly more popular in these regions. The application of an innovative PPP funding policy to upgrade London Underground's was shrouded in controversy since its announcement in the House of Commons by the then Labour government in March 1998. After an extended transaction period that resulted in significant transaction costs, three infrastructure contracts were signed in 2002/3. During the operational period of the PPP significant problems began to emerge between London Underground (LU) and its contractors. Four years into the first review period one consortium that was awarded two infrastructure contracts went into administration; and two years later the other consortium reverted back to the public sector operation. In sum the PPP model expected to deliver 15 billion pounds of investment into London Underground's ailing system was abandoned. The aim of this paper is to examine the lessons that can be learnt from the public sector's perspective when applying an innovative PPP model to upgrade LU's infrastructure. This will be achieved by investigating the factors that led to the PPP model being chosen in the first instance; and the series of events that led to its demise with a view to concluding whether it was a failure in the PPP policy or the implementation of the policy.
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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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.042 |
| Scholarly communication | 0.026 | 0.030 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".