Closing the books or keeping them open? Identity work in partner retirement from Big 4 accounting firms
Bibliographic record
Abstract
Abstract One view of the socialization experienced by professionals in global Big 4 firms suggests that the intensity of socialization engenders a strong and deep‐rooted professional identity. We scrutinize this claim by drawing on interviews with partners who retired from lifelong employment in Big 4 firms in Japan. Through partners' reflections on their experiences in detaching from the firm, we examine how socialization manifests in partners' identity work. We find that partners' identity, which often appears entrenched, invariable, and heroic, can be highly fragile and vulnerable to changing circumstances. Before leaving the firm, interviewees attempt to reconcile their Big 4 “graduation” with feelings of obsolescence and a growing distance from previous accomplishments. After leaving the firm, interviewees revisit the identity built throughout their careers. Unable to move on to a selfhood detached from that identity, they refashion their identity relative to their former Big 4 partner self, backgrounding their private life and post‐firm professional affiliations. Not knowing how to “close the books,” retired partners seek comfort in the old “plot” and in the old “characters,” finding ways to “keep the books open” even after the “setting” has changed. Our results reconfirm the powerful socialization experienced by partners during their tenure with the Big 4 but run counter to scholarship that characterizes the identity of Big 4 partners as strong and fixed. Rather, we demonstrate the insecurity underlying our professional service heroes' identity work and the contingent identity work processes that partners engage in while navigating departure from the Big 4.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| 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".