Bibliographic record
Abstract
Purpose Supreme audit institutions (SAIs) have become increasingly active in recent years in carrying out “performance audits” of various public bodies. But how does SAIs report on their own performance? The purpose of this paper is to report on a study (commissioned by the UK National Audit Office (NAO)) of how SAIs report on their own performance and explores a possible conceptual framework – a synthesis of work on “performance regimes”, “public value” and “competing values” approaches – which might provide a basis for enhancing such reporting. Design/methodology/approach The paper is based first on a review of self‐reporting of performance by SAIs in Australia, Canada, the USA, New Zealand and with a specific focus in more detail on the UK's NAO. In Section I, it explores existing self‐reporting practices of a number of SAIs based on their published reports. Section II of this paper is more conceptual. Drawing on notions of “performance regimes”, “public value” and “competing values”, it seeks to re‐conceptualise how SAIs in general, and the NAO specifically, might usefully report on their performance for multiple external audiences. Findings The conclusions drawn from the first part of the paper include that multiple measures of SAI performance have evolved, including impacts on governments; financial savings; impact on parliament; media impact, etc. The second part concludes tentatively that a synthesis of “public value” and “competing values” might provide a conceptual framework for making more sense of such multiple reporting. Practical implications The immediate practical value of this paper should be for SAIs in providing a possible framework for analysing and developing their own performance reporting policies to address multiple dimensions of achievement and meet the needs of multiple stake holders. More widely, this framework can be applied to other public agencies. Originality/value There are few, if any, current studies of comparative SAI self‐reporting of performance, so this paper makes a substantial contribution in this area. The conceptual framework developed in the second half of the paper is also unique in synthesising two important streams of thinking about “public value” and “competing values” which has far wider applicability than the study of SAIs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.079 | 0.295 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.028 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".