Embedding a “Reflexive Mindset”: Lessons From Reconfiguring the Internal Auditing Practice
Bibliographic record
Abstract
The education and training of internal auditors is an example of management learning which has received limited attention in management education journals. This paper presents the lessons from an action research inquiry designed to reconfigure the Internal Auditing function to address the problem of a conformance mindset and compliance-based approach. We show how cultivating a ‘reflexive mindset’ becomes a critical catalyst in developing an Internal Auditing approach that leads to the identification of misconduct, conduct risk and deficiencies in the organization’s conduct risk management and governance frameworks. We contribute to advance reflexivity as a practice that can support the reconfiguration of management functions like Internal Auditing, not only by readjusting operating routines but also by encouraging internal auditors to critically (re)consider how their activities may contribute to the common good of the organization’s members and customers.
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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 | 0.032 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.043 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.007 |
| 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; 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".