Impression Management in Annual Report Narratives: The Case of the UK Private Finance Initiative
Bibliographic record
Abstract
Purpose - The UK private finance initiative (PFI) public policy is heavily criticised. PFI contracts are highly profitable leading to incentives for PFI private-sector companies to support PFI public policy. This contested nature of PFIs requires legitimation by PFI private-sector companies, by means of impression management, in terms of the attention to and framing of PFI in PFI private-sector company annual reports. The paper aims to discuss this issue. Design/methodology/approach - PFI-related annual report narratives of three UK PFI private-sector companies, over seven years and across two periods of significant change in the development of the PFI public policy, are analysed using manual content analysis. Findings - Results suggest that PFI private-sector companies use impression management to legitimise during periods of uncertainty for PFI public policy, to alleviate concerns, to provide credibility for the policy and to legitimise the private sector’s own involvement in PFI. Research limitations/implications - While based on a sizeable database, the research is limited to the study of three PFI private-sector companies. Originality/value - The portrayal of public policy in annual report narratives has not been subject to prior research. The research demonstrates how managers of PFI private-sector companies present PFI narratives in support of public policy direction that, in turn, benefits PFI private-sector companies.
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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 | 0.018 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".