Auditors' Professional Identities: Review and Future Directions*
Bibliographic record
Abstract
Abstract Drawing on qualitative field studies, this literature review synthesizes research on the formation of professional identities. This journey is organized based on employee level, from the pre‐exposure phase of recruitment, up the hierarchy to staff‐, manager‐, and partner‐level work at auditing firms, to the level of those who leave professional service firms to pursue other work. Our analysis highlights the importance of acquiring soft skills over technical training in identity building, including the ability to incorporate unwritten rules and norms of professionalism that persist throughout professional careers. Our review reveals a scarcity of studies on the identity formation of managers. We also question the relevance of the literature on the socialization of staff auditors and partners and the impact on their identity. Critically, prior literature on auditor professional identity has emphasized one type of practitioner and setting—Western, urban, and Big 4 oriented—to the exclusion of other sites and perspectives. This emphasis has led to a disconnect between the extant research and the diversity of the realities in which auditing practices and auditors evolve. Thus, this review recognizes a need for new research directions and calls for research on professional services firms outside the Big 4 and in new and emerging markets. In addition, it advocates a greater focus on individuals and groups that have been excluded from prior research as the face of the profession changes.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".