A maturity level assessment of the use of technology by internal audit functions: a comparative analysis of the Federal Government of Canada
Bibliographic record
Abstract
This work presents the results of the empirical study conducted on internal audit (IA) functions in the Federal Government of Canada (after this Federal Government) to measure generalized audit software (GAS) use practices. The study empirically gauged the function maturity of the Federal Government Internal Audit. It sought to provide information on the current state and usage of GAS and the future needs of audit functions across the federal government. The current maturity assessment (2022) is phase two; phase one (2017) was completed five years ago. This work enables us to see if progress has been made in data analytics and provides valuable information on where to focus efforts to achieve best practices. People, processes and technology form the foundation of effective internal auditing. It is essential to continue assessing progress in these areas. This paper focuses on these three aspects, which contribute equally to the overall assessment of the maturity of GAS use by internal auditors in the Federal Government. The comparison drawn from the empirical findings indicates that there has not been significant progress in any area or overall maturity levels since the initial study in 2017. A comprehensive discussion of the results leads to policy recommendations for shaping the maturity-level assessment of future GAS use. At the same time, by considering Canada as an advanced country case study, the research aims to provide a lessons-learned experience from an organizational learning perspective for other countries and organizations while contributing to decision-making processes.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".