The Construction of Auditing Expertise in Measuring Government Performance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it