Revealing Oz: Institutional Work Shaping Auditors' National Office Consultations*
Bibliographic record
Abstract
ABSTRACT National office consultations (NOCs) are a mechanism intended to enhance audit quality, consistent with the logic of professionalism inherent in the audit profession. Yet research indicates that the competing logic of commercialism has become institutionalized in audit firms. We examine how the coexisting and conflicting logics of professionalism and commercialism manifest themselves in the current NOC dynamic. Specifically, we interview 22 highly experienced Big 4 audit firm partners to investigate how key actors engage in institutional work that creates, maintains, and disrupts the influence of professionalism and commercialism in NOC practices. We observe a swing of the pendulum: in the wake of SOX, audit firms adopted professionalism‐based practices which involved creating a more authoritative, “Oz”‐like national office identity, while in recent years key actors' institutional work reconfigured NOC practices and placed a renewed focus on commercialism. Our findings bring to light a number of implications that offer opportunities for future research. Although the new client‐inclusive culture aims to improve audit outcomes by encouraging consultations and fostering open dialogue with clients, it also exposes the national office to relationship‐management pressures and client‐service demands. Thus, practices developed to uphold professionalism also created a channel for commercialism‐focused practices, leading to unintended second‐order effects.
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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 | 0.019 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.022 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".