Caught between two worlds: Big 4 professionals moving to non–Big 4 firms
Bibliographic record
Abstract
Abstract Researchers have studied the entry of professionals into the public accounting field, their careers at an organization, and their exit from the field. However, they have largely overlooked the mobility of these professionals, whose careers involve firm transfers. Drawing on Bourdieu's sociology and interviews with 31 transferees and 7 non–Big 4 legacy partners, we examine the move of Big 4 professionals to non–Big 4 firms. Our findings show that transferees have an ingrained belief that a Big 4 career is the ideal professional trajectory. But they experience points of disjuncture at these firms, prompting them to reevaluate this organizational illusio and their career aspirations and ultimately reinforcing their transfer decision. After moving, transferees learn by trial and error how to valorize and layer their habitus and different forms of capital in order to adjust to the non–Big 4 firms. Our findings challenge prior assumptions about the superiority of Big 4 professionals and the distinctive forms of their capital by showing that the capital needed to obtain powerful positions at Big 4 and non–Big 4 firms are similar, but that its nature and relative value varies. Our findings reveal a paradoxical dynamic in which transferees' Big 4 habitus and capital undergo a complex, iterative process of valorization and layering when these professionals move within the public accounting field. This contrasts with a materialization of professional domination that occurs when former Big 4 employees move outside the public accounting field. For most of our transferees, dissonance also develops between the Big 4 and non–Big 4 layers of their habitus, and they never completely deconstruct their organizational illusio. These findings reveal that the reflexivity of transferees is both shaped and limited by their Big 4 habitus and illusio. Overall, our results contribute to the understanding of professional mobility within the public accounting field.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.007 | 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".