Using Their Discretion : How State Audit Institutions Determine Which Performance Audits to Undertake
Bibliographic record
Abstract
This chapter draws on examples from the United States, the United Kingdom, the Netherlands and Canada. It highlights the discretion available to auditors, which places them in a privileged position compared to most evaluators, and suggests that on its own the power to select topics is not enough to guarantee influence. The discretion allowed to auditors to make judgments about their study programs includes decisions about who is to make the choices, who should be consulted and what notice will be taken in response to their suggestions. The ability of state audit institutions (SAIs) to select their program—as a key element of their independence—is significant for a number of reasons. Statutory mandates provide SAIs with the right to undertake audits and report their findings. The legislation often provides a clear steer as to the subject matter to be chosen or the areas in which auditors can and cannot operate.
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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 | 0.048 | 0.157 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.027 | 0.013 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".