Accountability and Value for Money: A Framework for Exploring the Relationship in Private Finance Initiative Contracts
Bibliographic record
Abstract
There is an implicit assumption in the UK Treasury’s publications on public-private partnerships (PPP) – also more commonly known in the United Kingdom as private finance initiative (PFI) - that accountability and value for money (VFM) are related concepts. While recent academic studies on PPP/PFI (from now on as PFI) have focused on VFM, there is a notable absence of studies exploring the ‘presumed’ relationships between accountability and VFM. Drawing on Dubnick’s (Dubnick and Romzek, 1991, 1993; Dubnick, 1996, 1998, 2003, 2005; Dubnick and Justice, 2002) framework for accountability and PFI literature, we develop a research framework for exploring potential relationships between accountability and VFM in PFI projects by proposing alternative accountability cultures, processes and mechanisms for PFI. The PFI accountability model is then exposed to four criteria - warrantability, tractability, measurability and feasibility. Our preliminary interviews provide us guidance in identifying some of the cultures, processes and mechanisms indicated in our model which should enable future researchers to test not only the UK Government’s claimed relationships between accountability and VFM using more specific PFI empirical data, but also a potential relationship between accountability and performance in general.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".