Investigating the Regulation of Audit Quality in Canada: Complexity, Emotions, and Expertise
Bibliographic record
Abstract
I investigate the impact of regulation around audit quality in Canada on the work and perceptions of the stakeholders directly affected by these rules. More specifically, I examine auditors’ and audit committee (AC) members’ respective experience with: 1) National Instrument 52-108, which created the Canadian Public Accountability Board (CPAB) responsible for independently overseeing and inspecting auditors of Canadian reporting issuers; and 2) National Instrument 52-110, which notably sets requirements for the responsibilities of ACs as well as for the level of requisite expertise of AC members. Based on 48 interviews conducted with audit managers, audit partners, and AC members of Canadian reporting issuers, my analysis reveals the empirical depth of the Canadian audit regulation by shedding light on the actors, the processes, and the tensions embedded in the changes made to the environment of public companies. In my first study, I build on institutional theory to examine both the pressure brought by independent inspections and the changes implemented by audit firms in response to this added pressure. In my second study, I draw on Bourdieu’s scholarship to assess the effects of regulation and inspections on auditors’ experience of partnership and career trajectories. In my third study, I use a social constructivist approach to knowledge to analyze the way AC members perceive and enact their expert role in practice. My findings show that regulation has brought its share of complexity at the institutional, organizational, and individual levels. The new regulations have shaken the field by modifying the distribution of expert authority and status in the financial reporting system, which triggers a wide variety of emotional reactions. In this major overhaul, CPAB regulators have gained influence while audit partners have experienced a significant disjunction because of both regulators’ scrutiny and the renewed focus on the technical aspects of auditing. For their part, AC members have gained in expert status but struggle with uniformly embodying their expert role. Hence, AC members adopt different styles of oversight that do not require the same effort, knowledge, and focus, resulting in heterogeneous governance practices on ACs.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.021 | 0.018 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".