Does the UK Local Finance Improvement Trust (Lift) Initiative Improve Risk Management in Public-Private Procurement?
Bibliographic record
Abstract
The UK government introduced the Private Finance Initiative (PFI) and, latterly, the Local Improvement Finance Trust (LIFT) in an attempt to improve public service provision. As a variant of PFI, LIFT seeks to create a framework for the effective provision of primary care facilities. Like conventional PFI procurement, LIFT projects involve long-term contracts, complex multi-party interactions and thus create various risks to public sector clients. This paper investigates the advantages and disadvantages of LIFT with a focus on how this approach facilitates or impedes risk management from the public sector client perspective. Our paper concludes that LIFT has a potential for creating additional problems, including the further reduction of public sector control, conflicts of interest, the inappropriate use of enabling funds, and higher than market rental costs affecting the uptake of space in the buildings by local health care providers. However, there is also evidence that LIFT has facilitated new investment and that Primary Care Trusts (PCTs) have themselves started addressing some of the weaknesses of this procurement format through the bundling of projects and other forms of regional co-operation.
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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 | 0.073 | 0.146 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.020 | 0.016 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 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".