New Frontiers for Internal Audit Research<sup>*</sup>
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it