Professional Identity and Culture: A Review on Auditors' Professional Identities Through the Ranks and a Study on Auditors' Identities Away from Centers of Power
Bibliographic record
Abstract
In my thesis, I examine the behaviours that constitute proper professional conduct within professional service firms (PSFs). In the first study, I explore prior studies relating to professional socialization of auditors. Drawing on 22 qualitative field studies, I synthesize socialization through the ranks of PSFs. The levels of the journey range from students in prestigious Canadian business school learning how to present themselves to elite accounting firms, to partners who emphasize the increasing need to build the business at the expense of technical expertise. Critically, this work recognizes the historic overfocus on a specific type of firm – Big 4 offices in metropolitan locations, and the need to build a more complete perspective by cultivating new research by examining historically neglected firms, locations, and individuals.\nMy second study is based on 22 semi-structured interviews conducted with partners and senior managers at PSFs on the island of Newfoundland, where I was born and lived before moving to Ontario to attend Queen’s. I investigate the key attributes and skills that are needed to thrive in a PSF in Newfoundland. Newfoundland is a place far from centers of power, possessed of a unique culture and an economy starkly different from many prior sites of study. At the theoretical level, my study employs Bourdieu’s triad of Field, Capital, and Habitus to examine interviewees’ responses. I find that, contrary to prior studies of Western power-central locales, Newfoundland’s practitioners show an emphasis on client retention, on maintaining good relationships with other professionals, and on serving the community. Many of the forms of capital valued in other settings, such as displays of wealth or associations with elite educational institutions, are devalued in the Newfoundland setting. Likewise, interviewees often distanced themselves from traditional audit and assurance roles, deriding them as ‘non-value-added work’, while showing a preference for the trusted business advisor milieu.\nOverall, my dissertation contributes to our understanding of the linkage between local culture and the behaviour that is appropriate or required of accounting professionals. Newfoundland provides one example of a site ripe for further study, though other such sites abound. As my first paper asserts and my second further contends, a complete picture of the accounting profession can only be formed by recognizing the field beyond the metropole.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".