The Construction of Auditing Expertise in Measuring Government Performance
Bibliographic record
Abstract
Accounting research has increasingly been concerned to investigate professional expertise. This paper contributes to this interest by examining the process by which state (or government) auditors may become recognized as possessing expertise relevant to guiding and implementing new public management reforms. We analyze this process in the Canadian province of Alberta to understand the construction of a claim to expertise, drawing on theories in the social study of science and technology. Specifically, through our study of the development and consolidation of a network of support, we examine how the Office of the Auditor General of Alberta anchored its claims to expertise in the corpus of knowledge on measurement of government performance; the various devices that the Office used to sustain these claims; and the ways in which government and public servants reacted to them. In particular, our paper provides insights into how standards of “good practices” develop through a process of “fact” building, which involves the undertaking of local experiments by practitioners, the production of inscriptions in reports, and their subsequent validation by other practitioners. The production of inscriptions in sites of occupational practice such as those operated by government audit offices, and the collective process of validation that subsequently takes place in the practitioner community, are significant aspects in the construction of networks of support around claims to expertise.
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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 | 0.028 | 0.119 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.007 | 0.031 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".