Oligarchy in professional accounting bodies: Challenges for governance and leader‐member relations
Bibliographic record
Abstract
Abstract Drawing on Robert Michels's “iron law” of oligarchy, this study examines a governance crisis that unfolded at one of the world's largest professional accounting bodies (PABs)—CPA Australia. We leverage Michels's century‐old contribution to the social sciences to explore how this crisis sheds light on the challenges that PAB governance arrangements can pose when PAB leadership and membership priorities conflict. By applying Michels's seminal work to theorize the origins, escalation, leadership collapse, and eventual resolution of this PAB governance crisis, we illuminate how governance arrangements fueled conflict and fostered a democratic deficit that frustrated sections of the membership in their attempts to debate issues, exercise accountability on leadership matters, and become involved in governance reform. Overall, our analysis reveals that despite espoused principles of equity and participation, PABs are vulnerable to oligarchy impacting how their leaders relate to the interests of their members. Implications for the capacity of PABs to accommodate member conflict and for member participation in the current‐day professional context are discussed.
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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 | 0.022 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.044 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".