Bibliographic record
Abstract
Abstract Public sector audit is a vital activity within democratic states, which underpins the relationship between the government and the governed, the executive and the legislature, and different parts of the government. While there has been a lot of exceptional work in recent years on public sector audit, the sector faces new challenges. These challenges include regulatory space considerations, digitalization, the impact of service delivery design change, how audit and accountability arrangements address crises such as austerity, Brexit, black lives matter, climate change, disease in the form of COVID‐19 and war, and increased skepticism about the role of audit in society more generally. In this special issue, a group of scholars came together to describe this crisis in public audit, how the current literature addresses different facets of it and show how future research can contribute to analyzing it. This introductory article provides a brief summary of the current context of the “what, why, when, how, where and who” of public audit, before considering the contribution of each individual paper in this special issue to assisting in understanding the crisis of public audit, and finally setting out a conclusion and future directions.
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 | 0.034 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.029 |
| Scholarly communication | 0.021 | 0.025 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".