The Big 4 Under Pressure: Scanning Work in Transnational Fields*
Bibliographic record
Abstract
ABSTRACT We investigate what happens when accounting professionals come under external pressure to change established practices. We focus on corporate tax transparency, which has become an important battleground as stakeholders increasingly demand more information on corporate tax practices. While the Big 4 global accounting firms have traditionally played a dominant role in shaping what is perceived as acceptable corporate tax behavior, activists and critical politicians have recently mobilized public attention, challenging how accounting professionals legitimate their practices. We provide evidence of these challenges from 33 interviews and participation in 13 professional events from 2013 to 2019. We conceive of the confrontation between dominant professionals and challengers as taking place in a transnational “field,” where a range of actors struggle over how a common object—corporate tax transparency—is defined and treated. This approach helps us understand how the Big 4 navigate new challenges while seeking to maintain control over professional practices. Our interviews and observations show that Big 4 professionals are sensitive to political challenges, requiring that they engage in what we characterize as “scanning work”—ongoing activity to search for, identify, and assess challenges—to fend off outside interventions. Our analysis has important implications for further research. First, the need for scanning work when facing transnational political pressure implies a different way of seeing interactions between accounting professionals and (global) society at large. Second, viewing global accounting from a transnational field lens helps us identify complex sources of change external to already‐powerful actors.
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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 | 0.020 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.023 | 0.038 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".