Bibliographic record
Abstract
In the context of austerity‐inspired reforms to public audit in England we investigate the extent to which audit firms mitigate management bias in public sector financial reports. A substantial body of literature finds that both public and not‐for‐profit managers manage ‘earnings’ to report small surpluses close to zero by managing deficits upwards and surpluses downwards. Under agency theory, auditors acting in the interests of their principal(s) would tend to reverse this bias. We exploit privileged access to pre‐audit financial statements in the setting of the English National Health Service (NHS) to investigate the impact of audit adjustments on the pre‐audit financial statements of English NHS Foundation Trusts over the period 2010–2011 to 2014–2015. We find evidence that auditors act to reverse management bias in the case of Trusts with a pre‐audit deficit, but find no evidence that this is the case for Trusts with a pre‐audit surplus. In the case of Trusts in surplus, these findings are consistent with auditors’ interests being aligned with management, rather than principals.
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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 | 0.017 | 0.139 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".