“Believe it or not?”: The Emergence of Performance Information Auditing
Bibliographic record
Abstract
This chapter reviews the history of performance information auditing, discussing its genesis and what is driving its development. It examines the practice of performance information audit in relation to the traditional audit practice of providing financial audit opinions. The chapter presents and draws on the experiences of Western Australia and Canada. It addresses the question of where this audit practice is headed and if it all makes a difference. Through performance information auditing, the auditor provides assurance regarding the performance information of the reporting organization, with a focus on the quality of the information reported. Performance information audit follows in that tradition. In the New Zealand central government context the performance information subject to audit has emphasized outputs rather than outcomes, this arising from a public sector management framework that sees agencies responsible for outputs and the government, as a whole, responsible for outcomes.
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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.027 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.008 |
| 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".