Re-Examining Auditability through Auditors’ Responses to COVID-19: Roles and Limitations of Improvisation on the Production of Auditing Knowledge
Bibliographic record
Abstract
SUMMARY Drawing on Power’s theorization of the logic of auditability as a multidimensional system (Power 1996), we examine the impact of the COVID-19 pandemic on auditors’ year-end work from January to April 2020. Based on 24 semistructured interviews with auditing and accounting professionals located in China, we find that all four dimensions of the logic of auditability were destabilized at once. To restore the conditions of auditability during the pandemic, auditors improvised a deviant system of audit knowledge by rearranging the timeline of audit procedures, altering the substance of audit processes, and designing alternative control mechanisms. As the audit profession continues to evolve and more institutional decomposition (or reconfiguration) of the logic of auditability is expected to occur, this study contributes to our understanding of how auditors improvise in the backstage and produce comfort when they have to operate outside the protective umbrella of legitimate processes during sudden change of circumstances.
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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 | 0.040 | 0.102 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".