Lost in Evaluation: The Intricacies of First‐ and Second‐Order Evaluations in Auditors' Promotion Committees in the Big 4 Audit Firms
Bibliographic record
Abstract
ABSTRACT The evaluation of auditors in the Big 4 audit firms has largely remained a “black box” in accounting and audit research. Little is known about how these processes operate within audit firms or how they relate to promotion decisions. This study addresses this gap by providing direct insight into promotion committee decision‐making. Drawing on the sociology of evaluation, we develop an analytical model to map this process, highlighting the role of collective deliberation, the interplay between first‐ and second‐order evaluations, and the feedback effects of evaluation. Empirically, we rely on a unique multi‐method qualitative data set that focuses on the micro‐level dynamics of auditor evaluation and promotion. Our data includes non‐participant observation of two promotion committees in the Paris office of a Big 4 firm, complemented by 61 interviews. The findings show that auditors' evaluations are not solely based on pre‐assessments made by their direct supervisors but emerge through collective negotiation. This negotiation produces second‐order judgments that determine the auditors' final rankings. In these deliberations, the supervisor's voice and the political dynamics among supervisors carry significant weight. We conclude that the evaluation process hinges less on auditors' intrinsic professional qualities than on how managers' evaluative judgments are themselves assessed. These findings generate both empirical and theoretical contributions to the literature on auditing as a profession and on social evaluation.
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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 | 0.033 | 0.121 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".