Communication is a two-way street: Analyzing practices undertaken to systematically transfer audit research knowledge to policymakers
Bibliographic record
Abstract
Audit academics and policymakers express ongoing concerns about limited knowledge transfer between audit research and policymaking. We use theory-based knowledge transfer norms to evaluate eight practices used by audit academics to transfer research knowledge to policymakers. The discontinued PCAOB-AAA Auditing Section “Research Synthesis Project” came closest to enacting these theory-based knowledge transfer norms. Hence, we examine why those involved in that project did not follow these norms. Interviews with project authors reveal that their review creation approach anchored on the traditional academic literature review, and insufficiently adjusted it to meet the goal of communicating audit research evidence to policymakers. However, interviews with PCAOB project liaisons indicate that policymakers were engaged with and found value in the review creation process. Thus, we analyze PCAOB rulemaking documents for evidence that policymakers valued and used the project’s reviews in their policymaking. Over the project’s duration, we find increasing citations of research to the reviews themselves and to a broader set of academic research. Our findings of knowledge transfer occurrence in this project warrant further research on the efficacy of mobilizing audit research via research syntheses. We also show how several current audit domain knowledge transfer practices can be combined with the research syntheses approach leading to systematic effective knowledge transfer to policymakers.
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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 | 0.230 | 0.480 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.015 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.021 | 0.021 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".