Impression management in annual report narratives: the case of the UK private finance initiative
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it