Barriers to Transferring Auditing Research to Standard Setters
Bibliographic record
Abstract
Abstract Auditing researchers have published over 24,000 academic articles (Google Scholar September 2016) since 1970. Auditing standard setters and regulators frequently describe an inability to engage with and utilize this research to make evidence‐informed standard setting and regulatory decisions. For society to benefit from the large investment in audit research, the knowledge needs to systematically and effectively transferred to auditing policymakers. We draw on the knowledge transfer literature to identify barriers to transferring academic knowledge and discuss how these concepts apply to audit standard setting. We then examine a paradigmatic example of academic knowledge transfer to policymaking: evidence‐based medicine. Based on this analysis, we propose a tentative strategy to address the barriers to transferring audit research knowledge to policymaking and sketch out potential avenues for research. We conclude with an illustrative example of how to implement a knowledge transfer strategy that is effective in systematically transferring knowledge in other policymaking settings to the context of a specific audit standard‐setting project: group audits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.493 | 0.715 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.011 | 0.026 |
| Scholarly communication | 0.033 | 0.028 |
| Open science | 0.010 | 0.036 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier 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".