New Frontiers for Internal Audit Research<sup>*</sup>
Bibliographic record
Abstract
ABSTRACT Internal audit provides useful and valuable services to organizations, and academic research has established its importance in improving corporate governance. However, the body of internal audit research is still relatively small. Indeed, there are many emerging, lesser‐known topics and practitioners would like guidance. The primary focus of this paper is to make specific recommendations for future research based on surveys, interviews, and discussions with practitioners. We identify three broad areas for additional academic research: innovation in information technology, staffing and personnel development, and agile auditing. In each area, we describe current practices and discuss the relevant accounting literature, noting gaps where additional inquiry is needed. We also provide a list of testable research ideas to help inform academics about practice‐relevant research questions that would not only add to the academic literature, but would benefit practitioners who seek guidance. We hope this paper will inspire more academic research that investigates important internal audit questions.
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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 | 0.081 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.028 | 0.030 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".