Study on the reliability assessment and early-warning method of online auditing based on the perspective of IT control
Bibliographic record
Abstract
Purpose An important issue in online auditing is how to improve the reliability of online auditing in order to reduce the overall audit risk. In this paper, a reliability assessment and early-warning method of online auditing based on RC (rank centroid), AHP (analytic hierarchy process) and GM (1,1) is proposed from the perspective of information technology (IT) audit risk control. Design/methodology/approach The paper begins by structuring the AHP hierarchy to the reliability assessment of online auditing used in China. Then, RC is used to rank the importance of the assessment criteria. Pairwise comparisons of criteria are made based on the rank results of RC, and this leads to a matrix of comparisons. Next, the comparison matrices are translated into weights, and the reliability assessment and early-warning model of online auditing is constructed using the GM (1,1) model. A case illustration is given to analyze the application of this method. Findings Research results show that the reliability of the evaluation method designed in this paper is rigorous and effective. The reliability assessment and early-warning method of online auditing based on RC/AHP/GM (1,1) can assess and give an effective early warning of reliability changes in an online auditing system, which can meet the needs of current online auditing projects. Practical implications The results of this study have good potential for widespread future implementation of online auditing projects. Originality/value An effective reliability assessment and early-warning method of online auditing is proposed from the perspective of IT audit risk control in this study.
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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 | 0.017 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".