International Peer Review of the Performance Audit Practice of the United States Government Accountability Office
Bibliographic record
Abstract
Other written product issued by the Government Accountability Office with an abstract that begins "An international peer review team with representatives from the supreme audit institutions of Canada, Australia, Mexico, the Netherlands, Norway, South Africa, and Sweden reviewed the quality assurance system that the United States Government Accountability Office (GAO) has established for managing its performance audit practice. The GAO's quality assurance system encompasses its organizational structure and the policies and procedures established to provide it with reasonable assurance of complying with Government Auditing Standards. The GAO is responsible for the design of its quality assurance system and compliance with it, including the quality of its products. The responsibility of the peer review team is to express an opinion on whether the system is suitably designed and operating effectively to meet its objective. The criteria the peer review team used to assess the GAO's quality assurance system were drawn from GAO legislative authorities, Government Auditing Standards, and the GAO performance audit manual. The peer review team conducted the review in accordance with the peer review standards in Government Auditing Standards, and in a manner consistent with the Code of Ethics and auditing standards issued by the International Organization of Supreme Audit Institutions (INTOSAI). The peer review team examined the GAO's documented policies and procedures relative to applicable professional standards, reviewed documentation for a representative sample of 2004 audits, and interviewed professional and administrative staff."
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 | 0.042 | 0.167 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.031 | 0.020 |
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".