Evidence‐Informed Audit Standard Setting: Exploring Evidence Use and Knowledge Transfer*
Bibliographic record
Abstract
ABSTRACT Academics and practitioners agree that there are substantial barriers to systematically transferring audit research knowledge to policy‐makers. We adopt a design science approach to investigate the efficacy of employing a research synthesis, embedded in an interactive process with audit standard setters, to transfer such knowledge. We identify a standard‐setting issue that, we argue, is typical of the class of problems encountered by both parties when attempting knowledge transfer. Following design science prescriptions, we evaluate the pragmatic validity of our prototype research synthesis and its creation process. We provide initial evidence (“proof of concept”) that a research synthesis process can effectively and efficiently facilitate academic research knowledge transfer to inform audit standard setters' deliberations. Finally, we provide evidence that the problem of academic research knowledge transfer in accounting standard setting and evaluation continues. Our study and findings reflect how design science facilitates change in real‐world problem contexts through research‐based proofs of concept.
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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 | 0.577 | 0.710 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.028 | 0.030 |
| Open science | 0.007 | 0.025 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".