Navigating global–local epistemic tensions: how a Big Four shapes its IASB comment letters
Bibliographic record
Abstract
Purpose This study investigates the internal editorial process undertaken by a Big Four accounting firm (hereafter, Case Firm) in preparing global comment letters in response to the IASB's exposure drafts on revenue recognition and leases (IFRS 15 and 16). It examines global–local tensions through the lens of epistemic cultures, focusing on how local auditors and technical experts engage with and contest the firm's knowledge production practices. The study also explores how professional knowledge is constructed, legitimized, and circulated across geographically dispersed teams. Design/methodology/approach The study adopts a qualitative research design, drawing on semi-structured interviews with local IFRS experts and technical specialists from one of Case Firm's European Professional Practice Function (PPF). Data collection also includes internal document analysis and meeting observations, gathered during six months of immersive fieldwork. Findings Although local participants possess deep expertise in IFRSs, they perceive their technical contributions as undervalued in the global editorial process. Drafting is marked by negotiation, selective inclusion, and epistemic asymmetries that limit the influence of local perspectives. These tensions reflect clashing epistemic cultures within the firm, structured by spatial hierarchies and organizational dynamics that shape the recognition and circulation of knowledge. Originality/value Empirically, the paper offers rare insight into the backstage processes that shape Big Four firms' responses to global standard-setting initiatives. Theoretically, it extends research on global–local tensions in Global Professional Service Firms (GPSFs) by foregrounding the role of epistemic cultures in shaping technical decision-making. It further underscores the practical implications of perceived epistemic injustice for participants in GPSFs' knowledge-sharing initiatives.
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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 | 0.032 | 0.089 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.022 | 0.018 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.001 | 0.013 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".